{"title":"Machine learning","description":"Books on the subject of Machine learning","products":[{"product_id":"linear-algebra-and-learning-from-data-hardback-9780692196380","title":"Linear Algebra and Learning from Data (Hardback) 9780692196380","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eLinear Algebra and Learning from Data\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003eFrom Gilbert Strang, the first textbook that teaches linear algebra together with deep learning and neural nets.\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eGilbert Strang (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780692196380, Wellesley-Cambridge Press\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 31 January 2019\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e446 pages\u003cbr\u003e24.2 x 19.6 x 2.5 cm, 0.93 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eLinear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eDeep learning and neural nets\u003cbr\u003e Preface and acknowledgements\u003cbr\u003e Part I. Highlights of Linear Algebra\u003cbr\u003e Part II. Computations with Large Matrices\u003cbr\u003e Part III. Low Rank and Compressed Sensing\u003cbr\u003e Part IV. Special Matrices\u003cbr\u003e Part V. Probability and Statistics\u003cbr\u003e Part VI. Optimization\u003cbr\u003e Part VII. Learning from Data: Books on machine learning\u003cbr\u003e Eigenvalues and singular values\u003cbr\u003e Rank One\u003cbr\u003e Codes and algorithms for numerical linear algebra\u003cbr\u003e Counting parameters in the basic factorizations\u003cbr\u003e Index of authors\u003cbr\u003e Index\u003cbr\u003e Index of symbols.\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Pattern recognition [\u003ca title=\"See our other books on Pattern recognition\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Pattern%20recognition%20%5BUYQP%5D%22\"\u003eUYQP\u003c\/a\u003e], Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Maths for computer scientists [\u003ca title=\"See our other books on Maths for computer scientists\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Maths%20for%20computer%20scientists%20%5BUYAM%5D%22\"\u003eUYAM\u003c\/a\u003e], Mathematical modelling [\u003ca title=\"See our other books on Mathematical modelling\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mathematical%20modelling%20%5BPBWH%5D%22\"\u003ePBWH\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wellesley-Cambridge Press","offers":[{"title":"Default Title","offer_id":45999586640152,"sku":"9780692196380","price":60.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780692196380i_aa4a255b-2aa8-4513-a20b-c00e8c8fcd43.jpg?v=1691359193"},{"product_id":"inference-and-learning-from-data-9781009218108","title":"Inference and Learning from Data (Multiple-component retail product) 9781009218108","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eInference and Learning from Data\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003eDiscover core topics in inference and learning with this extraordinary three-volume set.\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eAli H. Sayed (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781009218108, Cambridge University Press\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eMultiple-component retail product, published 22 December 2022\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e3370 pages\u003cbr\u003e25.5 x 18 x 12 cm, 5.42 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e'The book series is timely and indispensable. It is a unique companion for graduate students and early-career researchers. The three volumes provide an extraordinary breadth and depth of techniques and tools, and encapsulate the experience and expertise of a world-class expert in the field. The pedagogically crafted text is written lucidly, yet never compromises rigor. Theoretical concepts are enhanced with illustrative figures, well-thought problems, intuitive examples, datasets, and MATLAB codes that reinforce readers' learning.' Abdelhak Zoubir, TU Darmstadt\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eThis extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. The first volume, Foundations, establishes core topics in inference and learning, and prepares readers for studying their practical application. The second volume, Inference, introduces readers to cutting-edge techniques for inferring unknown variables and quantities. The final volume, Learning, provides a rigorous introduction to state-of-the-art learning methods. A consistent structure and pedagogy is employed throughout all three volumes to reinforce student understanding, with over 1280 end-of-chapter problems (including solutions for instructors), over 600 figures, over 470 solved examples, datasets and downloadable Matlab code. Unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eVolume I. Foundations: 1. Matrix theory\u003cbr\u003e 2. Vector differentiation\u003cbr\u003e 3. Random variables\u003cbr\u003e 4. Gaussian distribution\u003cbr\u003e 5. Exponential distributions\u003cbr\u003e 6. Entropy and divergence\u003cbr\u003e 7. Random processes\u003cbr\u003e 8. Convex functions\u003cbr\u003e 9. Convex optimization\u003cbr\u003e 10. Lipschitz conditions\u003cbr\u003e 11. Proximal operator\u003cbr\u003e 12. Gradient descent method\u003cbr\u003e 13. Conjugate gradient method\u003cbr\u003e 14. Subgradient method\u003cbr\u003e 15. Proximal and mirror descent methods\u003cbr\u003e 16. Stochastic optimization\u003cbr\u003e 17. Adaptive gradient methods\u003cbr\u003e 18. Gradient noise\u003cbr\u003e 19. Convergence analysis I: stochastic gradient algorithms\u003cbr\u003e 20. Convergence analysis II: stochasic subgradient algorithms\u003cbr\u003e 21. Convergence analysis III: stochastic proximal algorithms\u003cbr\u003e 22. Variance-reduced methods I: uniform sampling\u003cbr\u003e 23. Variance-reduced methods II: random reshuffling\u003cbr\u003e 24. Nonconvex optimization\u003cbr\u003e 25. Decentralized optimization I: primal methods\u003cbr\u003e 26. Decentralized optimization II: primal-dual methods\u003cbr\u003e Author index\u003cbr\u003e Subject index. Volume II. Inference: 27. Mean-Square-Error inference\u003cbr\u003e 28. Bayesian inference\u003cbr\u003e 29. Linear regression\u003cbr\u003e 30. Kalman filter\u003cbr\u003e 31. Maximum likelihood\u003cbr\u003e 32. Expectation maximization\u003cbr\u003e 33. Predictive modeling\u003cbr\u003e 34. Expectation propagation\u003cbr\u003e 35. Particle filters\u003cbr\u003e 36. Variational inference\u003cbr\u003e 37. Latent Dirichlet allocation\u003cbr\u003e 38. Hidden Markov models\u003cbr\u003e 39. Decoding HMMs\u003cbr\u003e 40. Independent component analysis\u003cbr\u003e 41. Bayesian networks\u003cbr\u003e 42. Inference over graphs\u003cbr\u003e 43. Undirected graphs\u003cbr\u003e 44. Markov decision processes\u003cbr\u003e 45. Value and policy iterations\u003cbr\u003e 46. Temporal difference learning\u003cbr\u003e 47. Q-learning\u003cbr\u003e 48. Value function approximation\u003cbr\u003e 49. Policy gradient methods\u003cbr\u003e Author index\u003cbr\u003e Subject index. Volume III. Learning: 50. Least-squares problems\u003cbr\u003e 51. Regularization\u003cbr\u003e 52. Nearest-neighbor rule\u003cbr\u003e 53. Self-organizing maps\u003cbr\u003e 54. Decision trees\u003cbr\u003e 55. Naive Bayes classifier\u003cbr\u003e 56. Linear discriminant analysis\u003cbr\u003e 57. Principal component analysis\u003cbr\u003e 58. Dictionary learning\u003cbr\u003e 59. Logistic regression\u003cbr\u003e 60. Perceptron\u003cbr\u003e 61. Support vector machines\u003cbr\u003e 62. Bagging and boosting\u003cbr\u003e 63. Kernel methods\u003cbr\u003e 64. Generalization theory\u003cbr\u003e 65. Feed forward neural networks\u003cbr\u003e 66. Deep belief networks\u003cbr\u003e 67. Convolutional networks\u003cbr\u003e 68. Generative networks\u003cbr\u003e 69. Recurrent networks\u003cbr\u003e 70. Explainable learning\u003cbr\u003e 71. Adversarial attacks\u003cbr\u003e 72. Meta learning\u003cbr\u003e Author index\u003cbr\u003e Subject index.\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Signal processing [\u003ca title=\"See our other books on Signal processing\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Signal%20processing%20%5BUYS%5D%22\"\u003eUYS\u003c\/a\u003e], Pattern recognition [\u003ca title=\"See our other books on Pattern recognition\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Pattern%20recognition%20%5BUYQP%5D%22\"\u003eUYQP\u003c\/a\u003e], Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Communications engineering \/ telecommunications [\u003ca title=\"See our other books on Communications engineering \/ telecommunications\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Communications%20engineering%20\/%20telecommunications%20%5BTJK%5D%22\"\u003eTJK\u003c\/a\u003e], Information theory [\u003ca title=\"See our other books on Information theory\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Information%20theory%20%5BGPF%5D%22\"\u003eGPF\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":46002486542616,"sku":"9781009218108","price":167.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9781009218108i.jpg?v=1696791451"},{"product_id":"analysis-of-multivariate-and-high-dimensional-data-hardback-9780521887939","title":"Analysis of Multivariate and High-Dimensional Data (Hardback) 9780521887939","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eAnalysis of Multivariate and High-Dimensional Data\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003eThis modern approach integrates classical and contemporary methods, fusing theory and practice and bridging the gap to statistical learning.\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eInge Koch (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780521887939, Cambridge University Press\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 2 December 2013\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e526 pages, 5 b\/w illus.  98 colour illus.  76 tables  138 exercises\u003cbr\u003e26 x 18.2 x 2.8 cm, 1.29 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e'I must highly commend the author for writing an excellent comprehensive review of multivariate and high dimensional statistics … The lucid treatment and thoughtful presentation are two additional attractive features … Without any hesitation and with admiration, I would give the author a 10 out of 10 … The feat she has accomplished successfully for this difficult area of statistics is something very few could accomplish. The wealth of information is enormous and a motivated student can learn a great deal from this book … I highly recommend [it] to researchers working in the field of high dimensional data and to motivated graduate students.' Ravindra Khattree, International Statistical Review\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e'Big data' poses challenges that require both classical multivariate methods and contemporary techniques from machine learning and engineering. This modern text equips you for the new world - integrating the old and the new, fusing theory and practice and bridging the gap to statistical learning. The theoretical framework includes formal statements that set out clearly the guaranteed 'safe operating zone' for the methods and allow you to assess whether data is in the zone, or near enough. Extensive examples showcase the strengths and limitations of different methods with small classical data, data from medicine, biology, marketing and finance, high-dimensional data from bioinformatics, functional data from proteomics, and simulated data. High-dimension low-sample-size data gets special attention. Several data sets are revisited repeatedly to allow comparison of methods. Generous use of colour, algorithms, Matlab code, and problem sets complete the package. Suitable for master's\/graduate students in statistics and researchers in data-rich disciplines.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePart I. Classical Methods: 1. Multidimensional data\u003cbr\u003e 2. Principal component analysis\u003cbr\u003e 3. Canonical correlation analysis\u003cbr\u003e 4. Discriminant analysis\u003cbr\u003e Part II. Factors and Groupings: 5. Norms, proximities, features, and dualities\u003cbr\u003e 6. Cluster analysis\u003cbr\u003e 7. Factor analysis\u003cbr\u003e 8. Multidimensional scaling\u003cbr\u003e Part III. Non-Gaussian Analysis: 9. Towards non-Gaussianity\u003cbr\u003e 10. Independent component analysis\u003cbr\u003e 11. Projection pursuit\u003cbr\u003e 12. Kernel and more independent component methods\u003cbr\u003e 13. Feature selection and principal component analysis revisited\u003cbr\u003e Index.\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Probability \u0026amp; statistics [\u003ca title=\"See our other books on Probability \u0026amp; statistics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Probability%20\u0026amp;%20statistics%20%5BPBT%5D%22\"\u003ePBT\u003c\/a\u003e], Epidemiology \u0026amp; medical statistics [\u003ca title=\"See our other books on Epidemiology \u0026amp; medical statistics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Epidemiology%20\u0026amp;%20medical%20statistics%20%5BMBNS%5D%22\"\u003eMBNS\u003c\/a\u003e], Economic statistics [\u003ca title=\"See our other books on Economic statistics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Economic%20statistics%20%5BKCHS%5D%22\"\u003eKCHS\u003c\/a\u003e], Econometrics [\u003ca title=\"See our other books on Econometrics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Econometrics%20%5BKCH%5D%22\"\u003eKCH\u003c\/a\u003e], Data analysis: general [\u003ca title=\"See our other books on Data analysis: general\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Data%20analysis:%20general%20%5BGPH%5D%22\"\u003eGPH\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":46002712543512,"sku":"9780521887939","price":69.56,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780521887939i_e4f4f3e0-aff7-4ac0-90cb-69061a07519f.jpg?v=1691358894"},{"product_id":"phase-transitions-in-machine-learning-hardback-9780521763912","title":"Phase Transitions in Machine Learning (Hardback) 9780521763912","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003ePhase Transitions in Machine Learning\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003eThis state-of-the-art overview of the field describes how phase transitions occur and teaches appropriate methods for tackling the consequent problems.\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eLorenza Saitta (Author), Attilio Giordana (Author), Antoine Cornuéjols (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780521763912, Cambridge University Press\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 16 June 2011\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e410 pages, 90 b\/w illus.  10 tables\u003cbr\u003e25.4 x 19.5 x 2.7 cm, 1.1 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e\"... it is still an open question whether this will be one of the basic tools for understanding machine learning problems and methods in the future. Naturally, this book is an essential source for researchers who want to find answers to these questions.\"  \u003cbr\u003eJoe Hernandez-Orallo, Computing Reviews\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003ePhase transitions typically occur in combinatorial computational problems and have important consequences, especially with the current spread of statistical relational learning as well as sequence learning methodologies. In Phase Transitions in Machine Learning the authors begin by describing in detail this phenomenon, and the extensive experimental investigation that supports its presence. They then turn their attention to the possible implications and explore appropriate methods for tackling them. Weaving together fundamental aspects of computer science, statistical physics and machine learning, the book provides sufficient mathematics and physics background to make the subject intelligible to researchers in AI and other computer science communities. Open research issues are also discussed, suggesting promising directions for future research.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePreface\u003cbr\u003e Acknowledgements\u003cbr\u003e Notation\u003cbr\u003e 1. Introduction\u003cbr\u003e 2. Statistical physics and phase transitions\u003cbr\u003e 3. The satisfiability problem\u003cbr\u003e 4. Constraint satisfaction problems\u003cbr\u003e 5. Machine learning\u003cbr\u003e 6. Searching the hypothesis space\u003cbr\u003e 7. Statistical physics and machine learning\u003cbr\u003e 8. Learning, SAT, and CSP\u003cbr\u003e 9. Phase transition in FOL covering test\u003cbr\u003e 10. Phase transitions and relational learning\u003cbr\u003e 11. Phase transitions in grammatical inference\u003cbr\u003e 12. Phase transitions in complex systems\u003cbr\u003e 13. Phase transitions in natural systems\u003cbr\u003e 14. Discussions and open issues\u003cbr\u003e Appendix A. Phase transitions detected in two real cases\u003cbr\u003e Appendix B. An intriguing idea\u003cbr\u003e References\u003cbr\u003e Index.\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":46004997816600,"sku":"9780521763912","price":66.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780521763912i_98d4e9d3-679c-458a-9f9c-571a6724b09b.jpg?v=1691380846"},{"product_id":"a-descriptive-study-of-bengali-words-hardback-9781107064249","title":"A Descriptive Study of Bengali Words (Hardback) 9781107064249","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eA Descriptive Study of Bengali Words\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003eThis book sheds new light on the form and function of morphemes in construction of words in the Bengali language.\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eNiladri Sekhar Dash (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781107064249, Cambridge University Press\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 15 January 2015\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e372 pages\u003cbr\u003e23.7 x 15.9 x 3 cm, 0.74 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eThis book is a study of modern Bengali words based on the data obtained from a corpus of written texts. The author has used all kinds of data, information and examples from the Bengali corpus to shape up this text. He has made an empirical attempt to analyse Bengali words and other lexical items from the perspective of their surface orthographic representation to understand the internal structure of their composition with a focus on their functional roles in various contexts of their usage within texts. In order to achieve this goal, he has established a link between the internal composition and external representation of words within an interface of usage and function of words in texts. The issues addressed in the book include decomposition of words, interpretation of function of word-formative elements and analysis of lexico-semantic identities of the word-formative elements in relation to their function in words.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eAcknowledgements\u003cbr\u003e Bengali vowel sounds in cardinal diagram\u003cbr\u003e Roman and IPA codes for Bengali vowels and allographs\u003cbr\u003e Bengali consonants\u003cbr\u003e Roman and IPA codes for Bengali consonants\u003cbr\u003e Introduction\u003cbr\u003e 1. Word: a conceptual complexity\u003cbr\u003e 2. Usage of some word formative elements in Bengali\u003cbr\u003e 3. Frequency of use of words in Bengali\u003cbr\u003e 4. Structural components of Bengali words\u003cbr\u003e 5. Use of affixes with Bengali words\u003cbr\u003e 6. Postpositions used in Bengali\u003cbr\u003e 7. Compound nouns and adjectives\u003cbr\u003e 8. Structure of reduplicated forms in Bengali\u003cbr\u003e 9. Lexical naturalization in Bengali\u003cbr\u003e Appendices\u003cbr\u003e Bibliography\u003cbr\u003e Index.\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Natural language \u0026amp; machine translation [\u003ca title=\"See our other books on Natural language \u0026amp; machine translation\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Natural%20language%20\u0026amp;%20machine%20translation%20%5BUYQL%5D%22\"\u003eUYQL\u003c\/a\u003e], Applied linguistics for ELT [\u003ca title=\"See our other books on Applied linguistics for ELT\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Applied%20linguistics%20for%20ELT%20%5BEBAL%5D%22\"\u003eEBAL\u003c\/a\u003e], Computational linguistics [\u003ca title=\"See our other books on Computational linguistics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computational%20linguistics%20%5BCFX%5D%22\"\u003eCFX\u003c\/a\u003e], Grammar, syntax \u0026amp; morphology [\u003ca title=\"See our other books on Grammar, syntax \u0026amp; morphology\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Grammar,%20syntax%20\u0026amp;%20morphology%20%5BCFK%5D%22\"\u003eCFK\u003c\/a\u003e], Phonetics, phonology [\u003ca title=\"See our other books on Phonetics, phonology\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Phonetics,%20phonology%20%5BCFH%5D%22\"\u003eCFH\u003c\/a\u003e], Semantics, discourse analysis, etc [\u003ca title=\"See our other books on Semantics, discourse analysis, etc\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Semantics,%20discourse%20analysis,%20etc%20%5BCFG%5D%22\"\u003eCFG\u003c\/a\u003e], Language acquisition [\u003ca title=\"See our other books on Language acquisition\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Language%20acquisition%20%5BCFDC%5D%22\"\u003eCFDC\u003c\/a\u003e], Linguistics [\u003ca title=\"See our other books on Linguistics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Linguistics%20%5BCF%5D%22\"\u003eCF\u003c\/a\u003e], Language [\u003ca title=\"See our other books on Language\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Language%20%5BC%5D%22\"\u003eC\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":46006020178200,"sku":"9781107064249","price":51.89,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9781107064249i_fdd27346-e623-41c8-81ec-4e66c97042e0.jpg?v=1691361703"},{"product_id":"data-science-analytics-and-machine-learning-with-r-paperback-9780128242711","title":"Data Science, Analytics and Machine Learning with R (Paperback) 9780128242711","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eData Science, Analytics and Machine Learning with R\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003eOffers a practical R-based toolkit for data analysis using different machine learning techniques\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eLuiz Paulo Favero (Author), Patricia Belfiore (Author), Rafael de Freitas Souza (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780128242711, Elsevier Science\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback, published 25 January 2023\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e660 pages, 400 illustrations (200 in full color)\u003cbr\u003e27.6 x 21.6 x 4 cm, 1.77 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003ci\u003eData Science, Analytics and Machine Learning with R\u003c\/i\u003e explains the principles of data mining and machine learning techniques and accentuates the importance of applied and multivariate modeling. The book emphasizes the fundamentals of each technique, with step-by-step codes and real-world examples with data from areas such as medicine and health, biology, engineering, technology and related sciences. Examples use the most recent R language syntax, with recognized robust, widespread and current packages. Code scripts are exhaustively commented, making it clear to readers what happens in each command. For data collection, readers are instructed how to build their own robots from the very beginning.\u003c\/p\u003e  \u003cp\u003eIn addition, an entire chapter focuses on the concept of spatial analysis, allowing readers to build their own maps through geo-referenced data (such as in epidemiologic research) and some basic statistical techniques. Other chapters cover ensemble and uplift modeling and GLMM (Generalized Linear Mixed Models) estimations, both linear and nonlinear.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cb\u003ePart I: Introduction\u003c\/b\u003e\u003cbr\u003e1. Overview of Data Science, Analytics, and Machine Learning\u003cbr\u003e2. Introduction to the R Language\u003cbr\u003e\u003cbr\u003e\u003cb\u003ePart II: Applied Statistics and Data Visualization\u003c\/b\u003e\u003cbr\u003e3. Variables and Measurement Scales\u003cbr\u003e4. Descriptive and Probabilistic Statistics\u003cbr\u003e5. Hypotheses Tests\u003cbr\u003e6. Data Visualization and Multivariate Graphs\u003cbr\u003e\u003cbr\u003e\u003cb\u003ePart III: Data Mining and Preparation\u003c\/b\u003e\u003cbr\u003e7. Building Handcrafted Robots\u003cbr\u003e8. Using APIs to Collect Data\u003cbr\u003e9. Managing Data\u003cbr\u003e\u003cbr\u003e\u003cb\u003ePart IV: Unsupervised Machine Learning Techniques\u003c\/b\u003e\u003cbr\u003e10. Cluster Analysis\u003cbr\u003e11. Factorial and Principal Component Analysis (PCA)\u003cbr\u003e12. Association Rules and Correspondence Analysis\u003cbr\u003e\u003cbr\u003e\u003cb\u003ePart V: Supervised Machine Learning Techniques\u003c\/b\u003e\u003cbr\u003e13. Simple and Multiple Regression Analysis\u003cbr\u003e14. Binary, Ordinal and Multinomial Regression Analysis\u003cbr\u003e15. Count-Data and Zero-Inflated Regression Analysis\u003cbr\u003e16. Generalized Linear Mixed Models\u003cbr\u003e\u003cbr\u003e\u003cb\u003ePart VI: Improving Performance and Introduction to Deep Learning\u003c\/b\u003e\u003cbr\u003e17. Support Vector Machine\u003cbr\u003e18. CART (Classification and Regression Trees)\u003cbr\u003e19. Bagging, Boosting and Uplift (Persuasion) Modeling\u003cbr\u003e20. Random Forest\u003cbr\u003e21. Artificial Neural Network\u003cbr\u003e22. Introduction to Deep Learning\u003cbr\u003e\u003cbr\u003e\u003cb\u003ePart VII: Spatial Analysis\u003c\/b\u003e\u003cbr\u003e23. Working on Shapefiles\u003cbr\u003e24. Dealing with Simple Features Objects\u003cbr\u003e25. Raster Objects\u003cbr\u003e26. Exploratory Spatial Analysis\u003cbr\u003e\u003cbr\u003e\u003cb\u003ePart VII: Adding Value to your Work\u003c\/b\u003e\u003cbr\u003e27. Enhanced and Interactive Graphs\u003cbr\u003e28. Dashboards with R\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Academic Press","offers":[{"title":"Default Title","offer_id":46648080072984,"sku":"9780128242711","price":101.39,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780128242711.jpg?v=1694088133"},{"product_id":"data-mining-practical-machine-learning-tools-and-techniques-paperback-9780128042915","title":"Data Mining; Practical Machine Learning Tools and Techniques (Paperback) 9780128042915","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eData Mining\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003ePractical Machine Learning Tools and Techniques\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cem\u003e\u003cp\u003eThis highly anticipated fourth edition of the most acclaimed work on data mining and machine learning provides practical advice and techniques\u003c\/p\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eIan H. Witten (Author), Eibe Frank (Author), Mark A. Hall (Author), Christopher J. Pal (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780128042915, Elsevier Science\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback, published 20 December 2016\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e654 pages\u003cbr\u003e23.5 x 19 x 4 cm, 1.25 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\"...this volume is the most accessible introduction to data mining to appear in recent years. It is worthy of a fourth edition.\" \u003cb\u003e--Computing Reviews\u003c\/b\u003e\u003c\/p\u003e\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003ci\u003eData Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, \u003c\/i\u003eoffers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.\u003c\/p\u003e  \u003cp\u003eExtensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research.\u003c\/p\u003e  \u003cp\u003ePlease visit the book companion website at https:\/\/www.cs.waikato.ac.nz\/~ml\/weka\/book.html.\u003c\/p\u003e  \u003cp\u003eIt contains\u003c\/p\u003e \u003cul\u003e \u003cli\u003ePowerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book\u003c\/li\u003e \u003cli\u003eOnline Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book\u003c\/li\u003e \u003cli\u003eTable of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc.\u003c\/li\u003e\n\u003c\/ul\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003ePart I: Introduction to data mining \u003c\/b\u003e1. What’s it all about? 2. Input: Concepts, instances, attributes 3. Output: Knowledge representation 4. Algorithms: The basic methods 5. Credibility: Evaluating what’s been learned\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II. More advanced machine learning schemes \u003c\/b\u003e6. Trees and rules 7. Extending instance-based and linear models 8. Data transformations 9. Probabilistic methods 10. Deep learning 11. Beyond supervised and unsupervised learning 12. Ensemble learning 13. Moving on: applications and beyond\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Databases [\u003ca title=\"See our other books on Databases\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Databases%20%5BUN%5D%22\"\u003eUN\u003c\/a\u003e], Information technology: general issues [\u003ca title=\"See our other books on Information technology: general issues\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Information%20technology:%20general%20issues%20%5BUB%5D%22\"\u003eUB\u003c\/a\u003e], Library, archive \u0026amp; information management [\u003ca title=\"See our other books on Library, archive \u0026amp; information management\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Library,%20archive%20\u0026amp;%20information%20management%20%5BGLC%5D%22\"\u003eGLC\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Morgan Kaufmann","offers":[{"title":"Default Title","offer_id":46648082497816,"sku":"9780128042915","price":45.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780128042915.jpg?v=1694088154"},{"product_id":"artificial-intelligence-a-new-synthesis-paperback-9781558605350","title":"Artificial Intelligence: A New Synthesis (Paperback \/ softback) 9781558605350","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eArtificial Intelligence: A New Synthesis\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eNils J. Nilsson (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781558605350, Elsevier Science\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback \/ softback, published 17 April 1998\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e513 pages\u003cbr\u003e24.4 x 17.5 x 3.2 cm, 0.9 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eIntelligent agents are employed as the central characters in this introductory text. Beginning with elementary reactive agents, Nilsson gradually increases their cognitive horsepower to illustrate the most important and lasting ideas in AI. Neural networks, genetic programming, computer vision, heuristic search, knowledge representation and reasoning, Bayes networks, planning, and language understanding are each revealed through the growing capabilities of these agents. A distinguishing feature of this text is in its evolutionary approach to the study of AI. This book provides a refreshing and motivating synthesis of the field by one of AI's master expositors and leading researches.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eReactive Machines. Neural Networks. Machine Evolution. State Machines. Robot Vision. Search in State Spaces. Agents that Plan. Uninformed Search. Heuristic Search. Planning, Acting and Learning. Alternative Search. Knowledge Representation and Reasoning. The Propositional Calculus. The Predicate Calculus. Knowledge-based Systems. Representing Common sense Knowledge. Reasoning with Uncertain Information. Learning and Acting with Bayes Nets. Planning Methods Based on Logic. The Situation Calculus. Planning. Communication and Integration. Multiple Agents. Communication Among Agents. Agent Architectures.\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Expert systems \/ knowledge-based systems [\u003ca title=\"See our other books on Expert systems \/ knowledge-based systems\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Expert%20systems%20\/%20knowledge-based%20systems%20%5BUYQE%5D%22\"\u003eUYQE\u003c\/a\u003e], Artificial intelligence [\u003ca title=\"See our other books on Artificial intelligence\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Artificial%20intelligence%20%5BUYQ%5D%22\"\u003eUYQ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Morgan Kaufmann","offers":[{"title":"Default Title","offer_id":46648102224152,"sku":"9781558605350","price":47.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9781558605350.jpg?v=1696713816"},{"product_id":"blondie24-playing-at-the-edge-of-ai-paperback-9781558607835","title":"Blondie24; Playing at the Edge of AI (Paperback \/ softback) 9781558607835","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eBlondie24\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003ePlaying at the Edge of AI\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cem\u003e\"...an interesting companion to Prey, by Michael Crichton.\"   IEEE Spectrum Magazine, Feb. 2003\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eDavid B. Fogel (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781558607835, Elsevier Science\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback \/ softback, published 4 October 2001\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e406 pages\u003cbr\u003e21.6 x 14 x 2.6 cm, 0.41 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e\"Meet Blondie. She's a 24-year old graduate student in mathematics at the University of California at San Diego. She skis and surfs, and is an ace at math. But her real claim to fame is her amazing ability to play checkers. She's really good--not good enough to defeat a grand master, but she did earn a spot in the top 500 of an international checkers tournament. Considering that she taught herself how to play without reading books, taking classes, or getting tips from experienced players--that's impressive. And considering that Blondie is only a computer program, and the rest of her persona is just a product my imagination, you might say that's really impressive!\" \u003cb\u003e--from the Introduction\u003c\/b\u003e\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003ci\u003eBlondie24\u003c\/i\u003e tells the story of a computer that taught itself to play checkers far better than its creators ever could by using a program that emulated the basic principles of Darwinian evolution--random variation and natural selection-- to discover on its own how to excel at the game.   Unlike Deep Blue, the celebrated chess machine that beat Garry Kasparov, the former world champion chess player, this evolutionary program didn't have access to strategies employed by human grand masters, or to databases of moves for the endgame moves, or to other human expertise about the game of chekers. With only the most rudimentary information programmed into its \"brain,\" Blondie24 (the program's Internet username) created its own means of evaluating the complex, changing patterns of pieces that make up a checkers game by evolving artificial neural networks---mathematical models that loosely describe how a brain works.  It's fitting that \u003ci\u003eBlondie24\u003c\/i\u003e should appear in 2001, the year when we remember Arthur C. Clarke's prediction that one day we would succeed in creating a thinking machine. In this compelling narrative, David Fogel, author and co-creator of Blondie24, describes in convincing detail how evolutionary computation may help to bring us closer to Clarke's vision of HAL. Along the way, he gives readers an inside look into the fascinating history of AI and poses provocative questions about its future.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePart 1 - Setting the Stage Chapter 1 - Intelligent Machines: Imitating Life Chapter 2 - Deep Blue: A Triumph of AI? Chapter 3 - Building An Artificial Brain Chapter 4 - Evolutionary Computation: Putting Nature to Work Chapter 5 - Blue Hawaii: Natural Selection Chapter 6 - Checkers Chapter 7 - Chinook: The Man-machine Checkers Champion Chapter 8 - Samuel's Learning Machine Chapter 9 - The Samuel-Newell ChallengePart 2 - The Making of Blondie Chapter 10 - Evolving in the Checkers Environment Chapter 11 - In The Zone Chapter 12 - A Repeat Performance Chapter 13 - A New Dimension Chapter 14 - Letting the Genie Out of the Bottle Chapter 15 - Blondie24 Epilogue: The Future of Artificial Intelligence Appendix: Your Honor, I Object! Notes Index About the Author\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Expert systems \/ knowledge-based systems [\u003ca title=\"See our other books on Expert systems \/ knowledge-based systems\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Expert%20systems%20\/%20knowledge-based%20systems%20%5BUYQE%5D%22\"\u003eUYQE\u003c\/a\u003e], Artificial intelligence [\u003ca title=\"See our other books on Artificial intelligence\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Artificial%20intelligence%20%5BUYQ%5D%22\"\u003eUYQ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Morgan Kaufmann","offers":[{"title":"Default Title","offer_id":46648108482840,"sku":"9781558607835","price":19.89,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9781558607835_c5c4f5bc-9473-4d75-a09c-6654d4c909e3.jpg?v=1696713818"},{"product_id":"probabilistic-reasoning-in-intelligent-systems-networks-of-plausible-inference-paperback-9781558604797","title":"Probabilistic Reasoning in Intelligent Systems; Networks of Plausible Inference (Paperback \/ softback) 9781558604797","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eProbabilistic Reasoning in Intelligent Systems\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eNetworks of Plausible Inference\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eJudea Pearl (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781558604797, Elsevier Science\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback \/ softback, published 31 May 1997\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e584 pages\u003cbr\u003e22.9 x 15.1 x 3.6 cm, 0.75 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003ci\u003eProbabilistic Reasoning in Intelligent Systems\u003c\/i\u003e is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty.  The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. \u003c\/p\u003e\n\u003cp\u003eThe author distinguishes syntactic and semantic approaches to uncertainty--and offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational.  Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation.  Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognition--in short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eProbabilistic Reasoning in Intelligent Systems\u003c\/i\u003e will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences.  Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use.  The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eChapter 1  Uncertainty In AI Systems:  An Overview\u003cbr\u003eChapter 2  Bayesian Inference\u003cbr\u003eChapter 3  Markov and Bayesian Networks:  Two Graphical Representations of Probabilistic Knowledge\u003cbr\u003eChapter 4  Belief Updating by Network Propagation\u003cbr\u003eChapter 5  Distributed Revision of Composite Beliefs\u003cbr\u003eChapter 6  Decision and Control\u003cbr\u003eChapter 7  Taxonomic Hierarchies, Continuous Variables, and Uncertain Probabilities\u003cbr\u003eChapter 8  Learning Structure from Data\u003cbr\u003eChapter 9  Non-Bayesian Formalisms for Managing Uncertainty\u003cbr\u003eChapter 10  Logic and Probability:  The Strange Connection\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Expert systems \/ knowledge-based systems [\u003ca title=\"See our other books on Expert systems \/ knowledge-based systems\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Expert%20systems%20\/%20knowledge-based%20systems%20%5BUYQE%5D%22\"\u003eUYQE\u003c\/a\u003e], Artificial intelligence [\u003ca title=\"See our other books on Artificial intelligence\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Artificial%20intelligence%20%5BUYQ%5D%22\"\u003eUYQ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Morgan Kaufmann","offers":[{"title":"Default Title","offer_id":46648225595672,"sku":"9781558604797","price":45.28,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9781558604797.jpg?v=1694588820"},{"product_id":"data-science-concepts-and-practice-paperback-9780128147610","title":"Data Science; Concepts and Practice (Paperback) 9780128147610","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eData Science\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eConcepts and Practice\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cem\u003e\u003cp\u003eFully updated edition on data science concepts, strategies and techniques using open source software, RapidMiner\u003c\/p\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eVijay Kotu (Author), Bala Deshpande (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780128147610, Elsevier Science\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback, published 3 December 2018\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e568 pages\u003cbr\u003e23.4 x 19 x 3.5 cm, 1.09 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eLearn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform. Whether you are brand new to data science or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. \u003c\/p\u003e \u003cp\u003eData Science has become an essential tool to extract value from data for any organization that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, engineers, and analytics professionals and for anyone who works with data. \u003c\/p\u003e \u003cp\u003eYou’ll be able to:\u003c\/p\u003e \u003col\u003e  \u003cp\u003e \u003c\/p\u003e\n\u003cli\u003eGain the necessary knowledge of different data science techniques to extract value from data. \u003c\/li\u003e \u003cp\u003e \u003c\/p\u003e\n\u003cli\u003eMaster the concepts and inner workings of 30 commonly used powerful data science algorithms. \u003c\/li\u003e \u003cp\u003e \u003c\/p\u003e\n\u003cli\u003eImplement step-by-step data science process using using RapidMiner, an open source GUI based data science platform\u003c\/li\u003e \u003c\/ol\u003e \u003cp\u003eData Science techniques covered: Exploratory data analysis, Visualization, Decision trees, Rule induction, k-nearest neighbors, Naïve Bayesian classifiers, Artificial neural networks, Deep learning, Support vector machines, Ensemble models, Random forests, Regression, Recommendation engines, Association analysis, K-Means and Density based clustering, Self organizing maps, Text mining, Time series forecasting, Anomaly detection, Feature selection and more...\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e1. Introduction \u003cbr\u003e2. Data Science Process \u003cbr\u003e3. Data Exploration\u003cbr\u003e4. Classification \u003cbr\u003e5. Deep Learning \u003cbr\u003e6. Regression Methods \u003cbr\u003e7. Association Analysis \u003cbr\u003e8. Recommendation Engines \u003cbr\u003e9. Clustering \u003cbr\u003e10. Text Mining (renamed to: Natural Language Processing) \u003cbr\u003e11. Time Series Forecasting \u003cbr\u003e12. Anomaly Detection \u003cbr\u003e13. Feature Selection\u003cbr\u003e14. Model Evaluation \u003cbr\u003e15. Efficient Model Execution \u003cbr\u003e16. Getting Started with RapidMiner\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Information technology: general issues [\u003ca title=\"See our other books on Information technology: general issues\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Information%20technology:%20general%20issues%20%5BUB%5D%22\"\u003eUB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Morgan Kaufmann","offers":[{"title":"Default Title","offer_id":46648322195736,"sku":"9780128147610","price":50.79,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780128147610.jpg?v=1694090614"},{"product_id":"heuristic-search-theory-and-applications-hardback-9780123725127","title":"Heuristic Search; Theory and Applications (Hardback) 9780123725127","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eHeuristic Search\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eTheory and Applications\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cem\u003eYour guide to the analysis, implementation and application of heuristic search for artificial intelligence problem-solving techniques\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eStefan Edelkamp (Author), Stefan Schroedl (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780123725127, Elsevier Science\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 28 July 2011\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e712 pages\u003cbr\u003e23.4 x 19 x 3.7 cm, 1.6 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\"Heuristic Search is a very solid monograph and textbook on (not only heuristic) search. In its presentation it is always more formal than colloquial, it is precise and well structured. Due to its spiral approach it motivates reading it in its entirety.\" \u003cb\u003e--Zentralblatt MATH 2012\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\"The authors have done an outstanding job putting together this book on artificial intelligence (AI) heuristic state space search. It comprehensively covers the subject from its basics to the most recent work and is a great introduction for beginners in this field.\" \u003cb\u003e--BCS.org\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\"Heuristic search lies at the core of Artificial Intelligence and it provides the foundations for many different approaches in problem solving. This book provides a comprehensive yet deep description of the main algorithms in the field along with a very complete discussion of their main applications. Very well-written, it embellishes every algorithm with pseudo-code and technical studies of their theoretical performance.\" \u003cb\u003e--Carlos Linares López, Universidad Carlos III de Madrid\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\"This is an introduction to artificial intelligence heuristic state space search. Authors Edelkamp (U. of Bremen, Germany) and Schrödl (a research scientist at Yahoo! Labs) seek to strike a balance between search algorithms and their theoretical analysis, on the one hand, and their efficient implementation and application to important real-world problems on the other, while covering the field comprehensively from well-known basic results to recent work in the state of the art. Prior knowledge of artificial intelligence is not assumed, but basic knowledge of algorithms, data structures, and calculus is expected. Proofs are included for formal rigor and to introduce proof techniques to the reader. They have organized the material into five sections: heuristic search primer, heuristic search under memory constraints, heuristic search under time constraints, heuristic search variants, and applications.\" \u003cb\u003e--SciTech Book News\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\"This almost encyclopedic text is suitable for advanced courses in artificial intelligence and as a text and reference for developers, practitioners, students, and researchers in artificial intelligence, robotics, computational biology, and the decision sciences. The exposition is comparable to texts for a graduate-level or advanced undergraduate course in computer science, and prior exposure or coursework in advanced algorithms, computability, or artificial intelligence would help a great deal in understanding the material. Algorithms are described in pseudocode, accompanied by diagrams and narrative explanations in the text. The vast size of the ‘search algorithms’ subject domain and the variety of applications of search mean that much information--especially pertaining to applications of search algorithms--had to be left out; however, an extensive (though still limited) bibliography is included for follow-up by the reader. Exercises are provided for each chapter, except the five chapters on applications, and bibliographic notes accompany all chapters.\" \u003cb\u003e--Computing Reviews\u003c\/b\u003e\u003c\/p\u003e\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eSearch has been vital to artificial intelligence from the very beginning as a core technique in problem solving. The authors present a thorough overview of heuristic search with a balance of discussion between theoretical analysis and efficient implementation and application to real-world problems. Current developments in search such as pattern databases and search with efficient use of external memory and parallel processing units on main boards and graphics cards are detailed.\u003c\/p\u003e  \u003cp\u003eHeuristic search as a problem solving tool is demonstrated in applications for puzzle solving, game playing, constraint satisfaction and machine learning. While no previous familiarity with heuristic search is necessary the reader should have a basic knowledge of algorithms, data structures, and calculus. Real-world case studies and chapter ending exercises help to create a full and realized picture of how search fits into the world of artificial intelligence and the one around us.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003ePART I Heuristic Search Primer\u003c\/b\u003e  Chapter 1 Introduction Chapter 2 Basic Search Algorithms  Chapter 3 Dictionary Data Structures  Chapter 4 Automatically Created Heuristics\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART II Heuristic Search under Memory Constraints \u003c\/b\u003eChapter 5 Linear-Space Search Chapter 6 Memory Restricted Search  Chapter 7 Symbolic Search  Chapter 8 External Search \u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART III Heuristic Search under Time Constraints  \u003c\/b\u003eChapter 9 Distributed Search  Chapter 10 State Space Pruning Chapter 11 Real-Time Search by Sven Koenig \u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART IV Heuristic Search Variants  \u003c\/b\u003eChapter 12 Adversary Search  Chapter 13 Constraint Search  Chapter 14 Selective Search\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART V Heurstic Search Applications \u003c\/b\u003eChapter 15 Action Planning  Chapter 16 Automated System Verification  Chapter 17 Vehicle Navigation  Chapter 18 Computational Biology  Chapter 19 Robotics by Sven Koenig \u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Artificial intelligence [\u003ca title=\"See our other books on Artificial intelligence\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Artificial%20intelligence%20%5BUYQ%5D%22\"\u003eUYQ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Morgan Kaufmann","offers":[{"title":"Default Title","offer_id":46648338317592,"sku":"9780123725127","price":57.69,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780123725127.jpg?v=1694090696"},{"product_id":"c4-5-programs-for-machine-learning-paperback-9781558602380","title":"C4.5; Programs for Machine Learning (Paperback \/ softback) 9781558602380","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eC4.5\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003ePrograms for Machine Learning\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eJ. Ross Quinlan (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781558602380, Elsevier Science\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback \/ softback, published 2 December 1992\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e312 pages\u003cbr\u003e23.4 x 19 x 2 cm, 0.53 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eClassifier systems play a major role in machine learning and knowledge-based systems, and Ross Quinlan's work on ID3 and C4.5 is widely acknowledged to have made some of the most significant contributions to their development. This book is a complete guide to the C4.5 system as implemented in C for the UNIX environment. It contains a comprehensive guide to the system's use , the source code (about 8,800 lines), and implementation notes.\u003c\/p\u003e  \u003cp\u003eC4.5 starts with large sets of cases belonging to known classes. The cases, described by any mixture of nominal and numeric properties, are scrutinized for patterns that allow the classes to be reliably discriminated. These patterns are then expressed as models, in the form of decision trees or sets of if-then rules, that can be used to classify new cases, with emphasis on making the models understandable as well as accurate. The system has been applied successfully to tasks involving tens of thousands of cases described by hundreds of properties. The book starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting. Advantages and disadvantages of the C4.5 approach are discussed and illustrated with several case studies.\u003c\/p\u003e  \u003cp\u003eThis book should be of interest to developers of classification-based intelligent systems and to students in machine learning and expert systems courses.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e1  Introduction\u003cbr\u003e2  Constructing Decision Trees\u003cbr\u003e3  Unknown Attribute Values\u003cbr\u003e4  Pruning Decision Trees\u003cbr\u003e5  From Trees to Rules\u003cbr\u003e6  Windowing\u003cbr\u003e7  Grouping Attribute Values\u003cbr\u003e8  Interacting with Classification Models\u003cbr\u003e9  Guide to Using the System\u003cbr\u003e10  Limitations\u003cbr\u003e11  Desirable Additions\u003cbr\u003eAppendix:  Program Listings\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Expert systems \/ knowledge-based systems [\u003ca title=\"See our other books on Expert systems \/ knowledge-based systems\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Expert%20systems%20\/%20knowledge-based%20systems%20%5BUYQE%5D%22\"\u003eUYQE\u003c\/a\u003e], Artificial intelligence [\u003ca title=\"See our other books on Artificial intelligence\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Artificial%20intelligence%20%5BUYQ%5D%22\"\u003eUYQ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Morgan Kaufmann","offers":[{"title":"Default Title","offer_id":46648339759384,"sku":"9781558602380","price":42.89,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9781558602380.jpg?v=1696713811"},{"product_id":"machine-learning-guide-for-oil-and-gas-using-python-a-step-by-step-breakdown-with-data-algorithms-codes-and-applications-paperback-9780128219294","title":"Machine Learning Guide for Oil and Gas Using Python; A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications (Paperback) 9780128219294","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eMachine Learning Guide for Oil and Gas Using Python\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eA Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cem\u003eHelps readers learn how Python can solve practical applications of machine learning in the oil and gas industry\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eHoss Belyadi (Author), Alireza Haghighat (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780128219294, Elsevier Science\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback, published 13 April 2021\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e476 pages, 70 illustrations (50 in full color)\u003cbr\u003e22.9 x 15.1 x 3 cm, 0.77 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003ci\u003eMachine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications\u003c\/i\u003e delivers a critical training and resource tool to help engineers understand machine learning theory and practice, specifically referencing use cases in oil and gas. The reference moves from explaining how Python works to step-by-step examples of utilization in various oil and gas scenarios, such as well testing, shale reservoirs and production optimization. Petroleum engineers are quickly applying machine learning techniques to their data challenges, but there is a lack of references beyond the math or heavy theory of machine learning. \u003ci\u003eMachine Learning Guide for Oil and Gas Using Python\u003c\/i\u003e details the open-source tool Python by explaining how it works at an introductory level then bridging into how to apply the algorithms into different oil and gas scenarios. While similar resources are often too mathematical, this book balances theory with applications, including use cases that help solve different oil and gas data challenges.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e1. Introduction to Machine Learning and Python2. Data Import and Visualization3. Machine Learning Workflows and Types4. Unsupervised Machine Learning: Clustering Algorithms5. Supervised Learning6. Neural Networks7. Model Evaluation8. Fuzzy Logic9. Evolutionary Optimization\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Petroleum technology [\u003ca title=\"See our other books on Petroleum technology\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Petroleum%20technology%20%5BTHFP%5D%22\"\u003eTHFP\u003c\/a\u003e], Gas technology [\u003ca title=\"See our other books on Gas technology\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Gas%20technology%20%5BTHFG%5D%22\"\u003eTHFG\u003c\/a\u003e], Fossil fuel technologies [\u003ca title=\"See our other books on Fossil fuel technologies\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Fossil%20fuel%20technologies%20%5BTHF%5D%22\"\u003eTHF\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Gulf Professional Publishing","offers":[{"title":"Default Title","offer_id":46648347066648,"sku":"9780128219294","price":96.59,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780128219294.jpg?v=1694090743"},{"product_id":"the-era-of-artificial-intelligence-machine-learning-and-data-science-in-the-pharmaceutical-industry-paperback-9780128200452","title":"The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry (Paperback) 9780128200452","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eThe Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003eComprehensive overview on how Artificial Intelligence and Machine Learning can assist in driving and improving the drug discovery process\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eStephanie K. Ashenden (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780128200452, Elsevier Science\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback, published 28 April 2021\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e264 pages, 60 illustrations (20 in full color)\u003cbr\u003e23.4 x 19 x 1.8 cm, 0.56 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003ci\u003eThe Era of Artificial Intelligence, Machine Learning and Data Science in the Pharmaceutical Industry\u003c\/i\u003e examines the drug discovery process, assessing how new technologies have improved effectiveness. Artificial intelligence and machine learning are considered the future for a wide range of disciplines and industries, including the pharmaceutical industry. In an environment where producing a single approved drug costs millions and takes many years of rigorous testing prior to its approval, reducing costs and time is of high interest. This book follows the journey that a drug company takes when producing a therapeutic, from the very beginning to ultimately benefitting a patient’s life.\u003c\/p\u003e  \u003cp\u003eThis comprehensive resource will be useful to those working in the pharmaceutical industry, but will also be of interest to anyone doing research in chemical biology, computational chemistry, medicinal chemistry and bioinformatics.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e1. Introduction Drug Discovery 2. Intro to ML 3. Data Types 4. Target ID and Val 5. Hit Discovery 6. Lead Optimisation 7. Evaluating Safety and Toxicity 8. Precision Medicine and Finding the 'right patient': Date-driven Identification of Disease Subtypes 9. Image Analysis in Drug Discovery 10. Clinical Trials, Real World Evidence and Digital Medicine 11. To and From the Patient\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Expert systems \/ knowledge-based systems [\u003ca title=\"See our other books on Expert systems \/ knowledge-based systems\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Expert%20systems%20\/%20knowledge-based%20systems%20%5BUYQE%5D%22\"\u003eUYQE\u003c\/a\u003e], Artificial intelligence [\u003ca title=\"See our other books on Artificial intelligence\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Artificial%20intelligence%20%5BUYQ%5D%22\"\u003eUYQ\u003c\/a\u003e], Biotechnology [\u003ca title=\"See our other books on Biotechnology\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Biotechnology%20%5BTCB%5D%22\"\u003eTCB\u003c\/a\u003e], Pharmacology [\u003ca title=\"See our other books on Pharmacology\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Pharmacology%20%5BMMG%5D%22\"\u003eMMG\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Academic Press","offers":[{"title":"Default Title","offer_id":46648349065496,"sku":"9780128200452","price":86.29,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780128200452.jpg?v=1694090756"},{"product_id":"quantum-machine-learning-what-quantum-computing-means-to-data-mining-paperback-9780128100400","title":"Quantum Machine Learning; What Quantum Computing Means to Data Mining (Paperback) 9780128100400","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eQuantum Machine Learning\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eWhat Quantum Computing Means to Data Mining\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cem\u003eCaptures a broad array of highly specialized content in an accessible and up-to-date review of the growing academic field of quantum machine learning and its applications in industry\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003ePeter Wittek (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780128100400, Elsevier Science\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback, published 19 August 2016\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e176 pages\u003cbr\u003e22.9 x 15.1 x 1.3 cm, 0.23 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\"...represents a nice compact overview over the emerging eld of quantum machine learning for the interested reader.\" --\u003cb\u003eZentralblatt MATH\u003c\/b\u003e\u003c\/p\u003e\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003ci\u003eQuantum Machine Learning\u003c\/i\u003e bridges the gap between abstract developments in quantum computing and the applied research on machine learning. Paring down the complexity of the disciplines involved, it focuses on providing a synthesis that explains the most important machine learning algorithms in a quantum framework. Theoretical advances in quantum computing are hard to follow for computer scientists, and sometimes even for researchers involved in the field. The lack of a step-by-step guide hampers the broader understanding of this emergent interdisciplinary body of research. \u003c\/p\u003e  \u003cp\u003e\u003ci\u003eQuantum Machine Learning\u003c\/i\u003e sets the scene for a deeper understanding of the subject for readers of different backgrounds. The author has carefully constructed a clear comparison of classical learning algorithms and their quantum counterparts, thus making differences in computational complexity and learning performance apparent. This book synthesizes of a broad array of research into a manageable and concise presentation, with practical examples and applications.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eIntroductionChapter 1: Machine LearningChapter 2: Quantum MechanicsChapter 3: Quantum ComputingChapter 4: Unsupervised LearningChapter 5: Pattern Recognition and Neural NetworksChapter 6: Supervised Learning and SUpport Vector MachinesChapter 7: Regression AnalysisChapter 8: BoostingChapter 9: Clustering Structure and Quantum ComputingChapter 10: Quantum Pattern RecognitionChapter 11: Quantum ClassificationChapter 12: Quantum Process TomographyChapter 13: Boosting and Adiabatic Quantum Computing\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Quantum physics [\u003ca title=\"See our other books on Quantum physics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Quantum%20physics%20%5Bquantum%20mechanics%20\u0026amp;%20quantum%20field%20theory%5D%20%5BPHQ%5D%22\"\u003equantum mechanics \u0026amp; quantum field theory PHQ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Academic Press","offers":[{"title":"Default Title","offer_id":46648352014616,"sku":"9780128100400","price":55.49,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780128100400.jpg?v=1694090776"},{"product_id":"constraint-processing-hardback-9781558608900","title":"Constraint Processing (Hardback) 9781558608900","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eConstraint Processing\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003ePresents the first comprehensive examination of the theory that underlies constraint processing algorithms\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eRina Dechter (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781558608900, Elsevier Science\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 22 May 2003\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e512 pages\u003cbr\u003e23.4 x 18.6 x 3 cm, 1.03 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e“To summarize, this book distills well over three decades worth of development in CSP and constraint processing in a single textbook. I wholeheartedly recommend it to students, researchers and practitioners in artificial intelligence, constraint programming and operations research who want to know more about the theory of constraint processing.\" \u003cb\u003e--Roland H.C. Yap, National University of Singapore, in Theory and Practice of Logic Programming\u003c\/b\u003e“This book provides a comprehensive and much needed introduction to the field by one of its foremost experts. It is beautifully written and presents a unifying framework capturing a wide range of techniques for processing symbolic, numerical, and probabilistic information.? \u003cb\u003e--Bart Selman, Cornell University\u003c\/b\u003e“I’ve been waiting a long time for a good theoretical introduction to constraint programming. Rina Dechter’s book is just this. If you want to understand why this technology works, and how to make it work for you, then I recommend you read this book.? \u003cb\u003e--Toby Walsh, Cork Constraint Computation Centre\u003c\/b\u003e“The book is rigorous but it is not difficult to read. An abundance of examples illustrate concepts and algorithms. The reader is well guided through technical issues, so intuition is never hidden by technicalities.? \u003cb\u003e--Pedro Meseguer, Institut d’Investigació en Intellingència Artificial – Consejo Superior de Investigaciones Científicas (IIIA-CSIC)\u003c\/b\u003e“An indispensable resource for researchers and practitioners in AI and optimization.? \u003cb\u003e--Henry Kautz, University of Washington\u003c\/b\u003e“a welcome introduction to the field of constraint satisfaction that will help researchers, educators, and students understand what constraint processing is about. It is a comprehensive book that can be used as a companion for courses on constraint satisfaction especially because the reader does not need to be an expert in the area to understand the text. The introductory character of the book makes it easy to read; nevertheless advanced students and researchers may also find deeper information on some topics there. Rina Dechter is an excellent researcher with contributions in many areas of constraint satisfaction … I strongly recommend reading this book to everyone who wants to know what is behind constraint satisfaction technology and I think that this book should definitely be in the bookshelf of everyone who teaches constraint satisfaction.? \u003cb\u003e--Roman Barták, Faculty of Mathematics and Physics, Charles University in Prague, Czech Republic\u003c\/b\u003e\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eConstraint satisfaction is a simple but powerful tool. Constraints identify the impossible and reduce the realm of possibilities to effectively focus on the possible, allowing for a natural declarative formulation of what must be satisfied, without expressing how. The field of constraint reasoning has matured over the last three decades with contributions from a diverse community of researchers in artificial intelligence, databases and programming languages, operations research, management science, and applied mathematics. Today, constraint problems are used to model cognitive tasks in vision, language comprehension, default reasoning, diagnosis, scheduling, temporal and spatial reasoning. \u003cbr\u003e\u003cbr\u003eIn \u003ci\u003eConstraint Processing\u003c\/i\u003e, Rina Dechter, synthesizes these contributions, along with her own significant work, to provide the first comprehensive examination of the theory that underlies constraint processing algorithms. Throughout, she focuses on fundamental tools and principles, emphasizing the representation and analysis of algorithms.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePreface\u003cbr\u003e Introduction\u003cbr\u003e Constraint Networks\u003cbr\u003e Consistency-Enforcing Algorithms: Constraint Propagation\u003cbr\u003e Directional Consistency\u003cbr\u003e General Search Strategies\u003cbr\u003e General Search Strategies: Look-Back\u003cbr\u003e Local Search Algorithms\u003cbr\u003e Advanced Consistency Methods\u003cbr\u003e Tree-Decomposition Methods\u003cbr\u003e Hybrid of Search and Inference: Time-Space Trade-offs\u003cbr\u003e Tractable Constraint Languages\u003cbr\u003e Temporal Constraint Networks\u003cbr\u003e Constraint Optimization\u003cbr\u003e Probabilistic Networks\u003cbr\u003e Constraint Logic Programming\u003cbr\u003e Bibliography\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Expert systems \/ knowledge-based systems [\u003ca title=\"See our other books on Expert systems \/ knowledge-based systems\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Expert%20systems%20\/%20knowledge-based%20systems%20%5BUYQE%5D%22\"\u003eUYQE\u003c\/a\u003e], Artificial intelligence [\u003ca title=\"See our other books on Artificial intelligence\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Artificial%20intelligence%20%5BUYQ%5D%22\"\u003eUYQ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Morgan Kaufmann","offers":[{"title":"Default Title","offer_id":46648356503832,"sku":"9781558608900","price":53.37,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9781558608900_35813e41-22d9-4c61-a1b4-39524c4a2824.jpg?v=1696713831"},{"product_id":"predictive-analytics-and-data-mining-concepts-and-practice-with-rapidminer-paperback-9780128014608","title":"Predictive Analytics and Data Mining; Concepts and Practice with RapidMiner (Paperback \/ softback) 9780128014608","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003ePredictive Analytics and Data Mining\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eConcepts and Practice with RapidMiner\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cem\u003e\u003cp\u003eMaster predictive analysis through an easy to understand framework plus data mining using open source RapidMiner tools.\u003c\/p\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eVijay Kotu (Author), Bala Deshpande (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780128014608\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback \/ softback, published 5 December 2014\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e448 pages\u003cbr\u003e23.4 x 19 x 2.8 cm, 0.88 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\"...an excellent introductory data science textbook to expose students to the essential concepts in predictive analytics. For the seasoned professional, it can serve as a handy reference book to choose the best predictive analytics tool for a given data set.\" --\u003cb\u003eComputing Reviews\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\"... ideal for business users, data analysts, business analysts, business intelligence and data warehousing professionals and for anyone who wants to learn Data Mining.\" --\u003cb\u003eAnalyticBridge.com, 2015\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\"If learning-by-doing is your mantra -- as well it should be for predictive analytics -- this book will jumpstart your practice. Covering a broad, foundational collection of techniques, authors Kotu and Deshpande deliver crystal-clear explanations of the analytical methods that empower organizations to learn from data. After each concept, screenshots make the 'how to' immediately concrete, revealing the steps needed to set things up and go; you're guided through real hands-on execution.\" --\u003cb\u003eEric Siegel, Ph.D., founder of Predictive Analytics World and author of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\"The analytics that turns Big Data into actionable intelligence is no longer the exclusive realm of data scientists - it is impacting nearly every business function. Business intelligence is a significant competitive advantage, if applied properly.  Gaining that advantage requires that business decision makers and data analyst have a good understanding of the available analytics tools and how to apply them. Predictive Analytics and Data Mining book provides an easy to understand framework of predictive analytics and data mining concepts. The framework is reinforced with examples and sample datasets that demonstrate how to apply the new tools to real-world problems. I highly recommend this book to anyone who wants a better understanding of how to make analytics a game changer for your organization\"--\u003cb\u003eDavid Dowhan, President, TruSignal\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\"Predictive analytics and insights has become the most critical skill-set in decision making and running the modern business. Predictive Analytics and Data Mining provides you the advanced concepts and practical implementation techniques to incorporate analytics in your business process. The two dozen data mining algorithms covered in this book forms the underpinnings of the field of business analytics that has transformed the way data is treated in business. So far, advanced analytics has been practiced only by select few. With your interest and this book, you can master it too.\" --\u003cb\u003eSy Fahimi, Operating Partner \u0026amp; Executive-in-Residence, Symphony Technology Group\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\"There are two kinds of predictive analytics books.  One kind gives a high level informal overview to those who just want to understand this field conceptually.  Another kind gives a textbook technical introduction for the experts with extensive knowledge of statistics and computer science.  Kotu and Deshpande's book  is unusual because it strikes a nice balance.  It can be understood by anyone who can understand basic computations of probability.  The choice of RapidMiner is brilliant, since anyone who has used excel in the past can follow the hands-on examples, and learn to get more out of their data.   The book does an excellent job of appealing to our intuitions about probabilities and then expanding on these intuitions to cover very advanced topics, such as decision trees and association rules.  The material is accessible to anyone who took elementary statistics in college, even if college was many years ago.\" -\u003cb\u003e-Maria Stone, Vice President, Data and User Experience, Yahoo\u003c\/b\u003e\u003c\/p\u003e\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003ePut Predictive Analytics into Action\u003c\/b\u003eLearn the basics of Predictive Analysis and Data Mining through an easy to understand conceptual framework and immediately practice the concepts learned using the open source RapidMiner tool. Whether you are brand new to Data Mining or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Mining has become an essential tool for any enterprise that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, business intelligence and data warehousing professionals and for anyone who wants to learn Data Mining.You’ll be able to:1. Gain the necessary knowledge of different data mining techniques, so that you can select the right technique for a given data problem and create a general purpose analytics process.2. Get up and running fast with more than two dozen commonly used powerful algorithms for predictive analytics using practical use cases.3. Implement a simple step-by-step process for predicting an outcome or discovering hidden relationships from the data using RapidMiner, an open source GUI based data mining tool\u003c\/p\u003e  \u003cp\u003ePredictive analytics and Data Mining techniques covered: Exploratory Data Analysis, Visualization, Decision trees, Rule induction, k-Nearest Neighbors, Naïve Bayesian, Artificial Neural Networks, Support Vector machines, Ensemble models, Bagging, Boosting, Random Forests, Linear regression, Logistic regression, Association analysis using Apriori and FP Growth, K-Means clustering, Density based clustering, Self Organizing Maps, Text Mining, Time series forecasting, Anomaly detection and Feature selection. Implementation files can be downloaded from the book companion site at www.LearnPredictiveAnalytics.com\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003col\u003e \u003cli\u003eIntroduction\u003c\/li\u003e \u003cli\u003eData Mining Process\u003c\/li\u003e \u003cli\u003eData Exploration\u003c\/li\u003e \u003cli\u003eClassification\u003c\/li\u003e \u003cli\u003eRegression\u003c\/li\u003e \u003cli\u003eAssociation\u003c\/li\u003e \u003cli\u003eClustering\u003c\/li\u003e \u003cli\u003eModel Evaluation\u003c\/li\u003e \u003cli\u003eText Mining\u003c\/li\u003e \u003cli\u003eTime Series\u003c\/li\u003e \u003cli\u003eAnomaly Detection\u003c\/li\u003e \u003cli\u003eAdvanced Data Mining\u003c\/li\u003e \u003cli\u003eGetting Started with RapidMiner\u003c\/li\u003e\n\u003c\/ol\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Data mining [\u003ca title=\"See our other books on Data mining\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Data%20mining%20%5BUNF%5D%22\"\u003eUNF\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Freshly Printed Books","offers":[{"title":"Default Title","offer_id":46648480530712,"sku":"9780128014608","price":42.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780128014608_38786e94-fca5-414a-b60b-67291e8d2dab.jpg?v=1694353071"},{"product_id":"conformal-prediction-for-reliable-machine-learning-theory-adaptations-and-applications-paperback-9780123985378","title":"Conformal Prediction for Reliable Machine Learning; Theory, Adaptations and Applications (Paperback) 9780123985378","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eConformal Prediction for Reliable Machine Learning\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eTheory, Adaptations and Applications\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cem\u003eAn introduction to the theory of the conformal prediction framework, with practical applications in medicine, finance, and more\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eVineeth Balasubramanian (Edited by), Shen-Shyang Ho (Edited by), Vladimir Vovk (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780123985378, Elsevier Science\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback, published 10 June 2014\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e334 pages\u003cbr\u003e23.4 x 19 x 2.2 cm, 0.7 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\"...captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly detection.\" --\u003cb\u003eZentralblatt MATH, Sep-14\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\"...the book is highly recommended for people looking for formal machine learning techniques that can guarantee theoretical soundness and reliability.\" \u003cb\u003e--Computing Reviews,December 4,2014\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\"This book captures the basic theory of the framework, demonstrates how the framework can be applied to real-world problems, and also presents several adaptations of the framework…\"\u003cb\u003e --HPCMagazine.com, August 2014\u003c\/b\u003e\u003c\/p\u003e\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eThe conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction. \u003ci\u003eConformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications\u003c\/i\u003e captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly detection. As practitioners and researchers around the world apply and adapt the framework, this edited volume brings together these bodies of work, providing a springboard for further research as well as a handbook for application in real-world problems.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eSection I: Theory\u003c\/b\u003e 1. The Basic Conformal Prediction Framework 2. Beyond the Basic Conformal Prediction Framework\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection II: Adaptations\u003c\/b\u003e 3. Active Learning using Conformal Prediction 4. Anomaly Detection 5. Online Change Detection by Testing Exchangeability 6. Feature Selection and Conformal Predictors 7. Model Selection 8. Quality Assessment 9. Other Adaptations\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection III: Applications\u003c\/b\u003e 10. Biometrics 11. Diagnostics and Prognostics by Conformal Predictors 12. Biomedical Applications using Conformal Predictors 13. Reliable Network Traffic Classification and Demand Prediction 14. Other Applications\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Artificial intelligence [\u003ca title=\"See our other books on Artificial intelligence\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Artificial%20intelligence%20%5BUYQ%5D%22\"\u003eUYQ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Morgan Kaufmann","offers":[{"title":"Default Title","offer_id":46648481153304,"sku":"9780123985378","price":77.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780123985378.jpg?v=1694091834"},{"product_id":"genetic-programming-an-introduction-hardback-9781558605107","title":"Genetic Programming; An Introduction (Hardback) 9781558605107","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eGenetic Programming\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eAn Introduction\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eWolfgang Banzhaf (Author), Peter Nordin (Author), Robert E. Keller (Author), Frank D. Francone (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781558605107, Elsevier Science\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 24 February 1998\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e496 pages\u003cbr\u003e24.4 x 17.5 x 3 cm, 0.99 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e\"[The authors] have performed a remarkable double service with this excellent book on genetic programming. First, they give an up-to-date view of the rapidly growing field of automatic creation of computer programs by means of evolution and, second, they bring together their own innovative and formidable work on evolution of assembly language machine code and linear genomes.\" \u003cb\u003e--John R. Koza\u003c\/b\u003e\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eSince the early 1990s, genetic programming (GP)—a discipline whose goal is to enable the automatic generation of computer programs—has emerged as one of the most promising paradigms for fast, productive software development.   GP combines biological metaphors gleaned from Darwin's theory of evolution with computer-science approaches drawn from the field of machine learning to create programs that are capable of adapting or recreating themselves for open-ended tasks.This unique introduction to GP provides a detailed overview of the subject and its antecedents, with extensive references to the published and online literature.  In addition to explaining the fundamental theory and important algorithms, the text includes practical discussions covering a wealth of potential applications and real-world implementation techniques.  Software professionals needing to understand and apply GP concepts will find this book an invaluable practical and theoretical guide.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e1  Genetic Programming as Machine Learning\u003cbr\u003e2  Genetic Programming and Biology\u003cbr\u003e3  Computer Science and Mathematical Basics\u003cbr\u003e4  Genetic Programming as Evolutionary Computation\u003cbr\u003e5  Basic Concepts—The Foundation\u003cbr\u003e6  Crossover—The Center of the Storm\u003cbr\u003e7  Genetic Programming and Emergent Order\u003cbr\u003e8  Analysis—Improving Genetic Programming with Statistics\u003cbr\u003e9  Different Varieties of Genetic Programming\u003cbr\u003e10  Advanced Genetic Programming\u003cbr\u003e11  Implementation—Making Genetic Programming Work\u003cbr\u003e12  Applications of Genetic Programming\u003cbr\u003e13  Summary and Perspectives\u003cbr\u003eA  Printed and Recorded Resources\u003cbr\u003eB  Information Available on the Internet\u003cbr\u003eC  GP Software \u003cbr\u003eD  Events\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Computer programming \/ software development [\u003ca title=\"See our other books on Computer programming \/ software development\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20programming%20\/%20software%20development%20%5BUM%5D%22\"\u003eUM\u003c\/a\u003e], Mathematical logic [\u003ca title=\"See our other books on Mathematical logic\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mathematical%20logic%20%5BPBCD%5D%22\"\u003ePBCD\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Morgan Kaufmann","offers":[{"title":"Default Title","offer_id":46648483381528,"sku":"9781558605107","price":71.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9781558605107.jpg?v=1694588812"},{"product_id":"machine-learning-and-medical-imaging-hardback-9780128040768","title":"Machine Learning and Medical Imaging (Hardback) 9780128040768","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eMachine Learning and Medical Imaging\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cp\u003eLearn how to apply machine learning methods to medical imaging \u003c\/p\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eGuorong Wu (Edited by), Dinggang Shen (Edited by), Mert Sabuncu (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780128040768\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 11 August 2016\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e512 pages\u003cbr\u003e23.4 x 19 x 3 cm, 1.71 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003ci\u003eMachine Learning and Medical Imaging\u003c\/i\u003e presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, including not only classical probabilistic modeling and learning methods, but also recent breakthroughs in deep learning, sparse representation\/coding, and big data hashing. In the second part leading research groups around the world present a wide spectrum of machine learning methods with application to different medical imaging modalities, clinical domains, and organs.\u003c\/p\u003e  \u003cp\u003eThe biomedical imaging modalities include ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), histology, and microscopy images. The targeted organs span the lung, liver, brain, and prostate, while there is also a treatment of examining genetic associations. \u003ci\u003eMachine Learning and Medical Imaging\u003c\/i\u003e is an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePart 1: Cutting-Edge Machine Learning Techniques in Medical Imaging\u003c\/p\u003e \u003cp\u003eChapter 1: Functional connectivity parcellation of the human brain\u003c\/p\u003e \u003cp\u003eChapter 2: Kernel machine regression in neuroimaging genetics\u003c\/p\u003e \u003cp\u003eChapter 3: Deep learning of brain images and its application to multiple sclerosis\u003c\/p\u003e \u003cp\u003eChapter 4: Machine learning and its application in microscopic image analysis\u003c\/p\u003e \u003cp\u003eChapter 5: Sparse models for imaging genetics\u003c\/p\u003e \u003cp\u003eChapter 6: Dictionary learning for medical image denoising, reconstruction, and segmentation\u003c\/p\u003e \u003cp\u003eChapter 7: Advanced sparsity techniques in magnetic resonance imaging\u003c\/p\u003e \u003cp\u003eChapter 8: Hashing-based large-scale medical image retrieval for computer-aided diagnosis\u003c\/p\u003e \u003cp\u003ePart 2: Successful Applications in Medical Imaging\u003c\/p\u003e \u003cp\u003eChapter 9: Multitemplate-based multiview learning for Alzheimer’s disease diagnosis\u003c\/p\u003e \u003cp\u003eChapter 10: Machine learning as a means toward precision diagnostics and prognostics\u003c\/p\u003e \u003cp\u003eChapter 11: Learning and predicting respiratory motion from 4D CT lung images\u003c\/p\u003e \u003cp\u003eChapter 12: Learning pathological deviations from a normal pattern of myocardial motion: Added value for CRT studies?\u003c\/p\u003e \u003cp\u003eChapter 13: From point to surface: Hierarchical parsing of human anatomy in medical images using machine learning technologies\u003c\/p\u003e \u003cp\u003eChapter 14: Machine learning in brain imaging genomics\u003c\/p\u003e \u003cp\u003eChapter 15: Holistic atlases of functional networks and interactions (HAFNI)\u003c\/p\u003e \u003cp\u003eChapter 16: Neuronal network architecture and temporal lobe epilepsy: A connectome-based and machine learning study\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Image processing [\u003ca title=\"See our other books on Image processing\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Image%20processing%20%5BUYT%5D%22\"\u003eUYT\u003c\/a\u003e], Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Enterprise software [\u003ca title=\"See our other books on Enterprise software\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Enterprise%20software%20%5BUFL%5D%22\"\u003eUFL\u003c\/a\u003e], Medical bioinformatics [\u003ca title=\"See our other books on Medical bioinformatics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Medical%20bioinformatics%20%5BMBF%5D%22\"\u003eMBF\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Freshly Printed Books","offers":[{"title":"Default Title","offer_id":46648515068184,"sku":"9780128040768","price":78.66,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780128040768_6e3c4129-9850-4106-8f1c-8cbd5b345653.jpg?v=1694353161"},{"product_id":"machine-learning-and-data-science-in-the-oil-and-gas-industry-best-practices-tools-and-case-studies-paperback-9780128207147","title":"Machine Learning and Data Science in the Oil and Gas Industry; Best Practices, Tools, and Case Studies (Paperback) 9780128207147","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eMachine Learning and Data Science in the Oil and Gas Industry\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eBest Practices, Tools, and Case Studies\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cem\u003eHelps readers understand the terminology, technology and skills needed to apply machine learning to oil and gas operations\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003ePatrick Bangert (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780128207147, Elsevier Science\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback, published 8 March 2021\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e306 pages\u003cbr\u003e22.9 x 15.1 x 2 cm, 0.57 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003e\u003ci\u003eMachine Learning and Data Science in the Oil and Gas Industry \u003c\/i\u003e\u003c\/b\u003eexplains how machine learning can be specifically tailored to oil and gas use cases. Petroleum engineers will learn when to use machine learning, how it is already used in oil and gas operations, and how to manage the data stream moving forward. Practical in its approach, the book explains all aspects of a data science or machine learning project, including the managerial parts of it that are so often the cause for failure. Several real-life case studies round out the book with topics such as predictive maintenance, soft sensing, and forecasting. Viewed as a guide book, this manual will lead a practitioner through the journey of a data science project in the oil and gas industry circumventing the pitfalls and articulating the business value. \u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e1. Introduction\u003cbr\u003e2. Data Science, Statistics, and Time-Series\u003cbr\u003e3. Machine Learning\u003cbr\u003e4. Introduction to Machine Learning in the Oil and Gas Industry\u003cbr\u003e5. Data Management from the DCS to the Historian\u003cbr\u003e6. Getting the Most Across the Value Chain\u003cbr\u003e7. Getting the Most Across the Value Chain\u003cbr\u003e8. The Business of AI Adoption\u003cbr\u003e9. Global Practice of AI and Big Data in Oil and Gas Industry\u003cbr\u003e10. Soft Sensors for NOx Emissions\u003cbr\u003e11. Detecting Electric Submersible Pump Failures\u003cbr\u003e12. Predictive and Diagnostic Maintenance for Rod Pumps\u003cbr\u003e13. Forecasting Slugging in Gas Lift Wells\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Petroleum technology [\u003ca title=\"See our other books on Petroleum technology\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Petroleum%20technology%20%5BTHFP%5D%22\"\u003eTHFP\u003c\/a\u003e], Gas technology [\u003ca title=\"See our other books on Gas technology\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Gas%20technology%20%5BTHFG%5D%22\"\u003eTHFG\u003c\/a\u003e], Fossil fuel technologies [\u003ca title=\"See our other books on Fossil fuel technologies\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Fossil%20fuel%20technologies%20%5BTHF%5D%22\"\u003eTHF\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Gulf Professional Publishing","offers":[{"title":"Default Title","offer_id":46648527946008,"sku":"9780128207147","price":96.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780128207147.jpg?v=1694092097"},{"product_id":"automated-planning-theory-and-practice-hardback-9781558608566","title":"Automated Planning; Theory and Practice (Hardback) 9781558608566","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eAutomated Planning\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eTheory and Practice\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cem\u003eThe first book to go beyond classical planning, showcasing a range of modern techniques\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eMalik Ghallab (Author), Dana Nau (Author), Paolo Traverso (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781558608566, Elsevier Science\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 21 May 2004\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e672 pages\u003cbr\u003e23.4 x 19 x 3.6 cm, 1.52 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\"Automated Planning is a tremendous book! It provides an extremely comprehensive, systematic, and clear coverage of this important and exciting field of AI. Readers will not only gain a deep understanding of the theoretical foundations of planning; they will actually learn how this future-oriented technology is to be applied in a variety of areas. Automated Planning is just the standard AI planning textbook we have been waiting for.\" \u003cb\u003e--Professor Susanne Biundo, Director of PLANET, the European Network of Excellence in AI Planning \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e\"\u003c\/b\u003eThis book is an excellent resource for both students and teachers, and a valuable reference guide for seasoned planning researchers. It covers a surprising level of technical details for its size, yet is quite accessible to t the mathematically astute reader. I would like to thank the authors for making this body of knowledge accessible to a wider audience.\" \u003cb\u003e--Kutluhan Erol, Mindlore, Inc. \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePlanning research, which has been a key area in AI since the General Problem Solver of Newell and Simon in 50's, has undergone significant progress in the last few years. In this book, Malik Ghallab, Dana Nau, and Paolo Traverso, three leading AI planning researchers, provide the first balanced and comprehensive introduction to this exciting and fast moving field. \u003cb\u003e--Hector Geffner, Universitat Pompeu Fabra \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e\"\u003c\/b\u003eAI planning experts, teachers, and students have been waiting for ages for the first textbook about the field---the comprehensive, up-to-date synthesis. Here it is! An admirable piece of work that will undoubtedly become a standard reference.\" \u003cb\u003e--Joachim Hertzberg, Fraunhofer Institute for Autonomous Intelligent Systems \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\"A much needed and timely compendium that conveys both the diverse history and the current excitement of the research in Automated Planning.\" \u003cb\u003e--Subbarao Kambhampati, Arizona State University\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e\"\u003c\/b\u003eThe publication of this book creates an opportunity for planning to reach a much wider community than specialized researchers, to capture the imaginations of a new generation of AI students, both graduates and undergraduates, showing that while planning is rooted in strong theoretical foundations, its applications can reach from intelligent game play to evacuation operations and even to the stars! I am certain that it will find a place on the bookshelves of every serious planning researcher, but its true place is in the minds of our undergraduate and graduate students whom it should inspire to add to the impressive body of work it describes.\" \u003cb\u003e--Derek Long, University of Strathclyde \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\"In recent years, comprehensive texts have been written for several of the other major areas of Artificial Intelligence, including machine learning, natural-language processing, and constraint-satisfaction processing, but until now, the field of planning has been devoid of such a resource, despite the considerable number of advances in and the significant maturation of planning research in the past decade. With Automated Planning: Theory and Practice, Malik Ghallab, Dana Nau, and Paolo Traverso have filled that void, and have done so with a remarkably clear and well-written book.\" \u003cb\u003e--Martha Pollack, University of Michigan\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\"The authors cover a vast range of topics in planning research. The reader will find methodical formalisms of theoretical concepts with illustrative examples, as well as practical case studies. Well-developed exercises provide practice for students of planning. This is a great book.\" \u003cb\u003e--Stephen Smith, Great Game Products \u003c\/b\u003e\"The engineer who needs to know how to use AI planning is on his own, in the tangled forest of undigested reports of original research.Until now, that is. For the first time there is a text that is comprehensive, structured and up to date. Automated Planning provides both the graduate student of AI and the engineer faced with designing an autonomous system with the reference they really need.\" \u003cb\u003e--Sam Steele, University of Essex \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e\"\u003c\/b\u003ePlanning is one of the most important aspects of intelligent behavior. AI techniques to automate it are a significant challenge. This textbook guides you through the advances made in 40 years of pioneering R\u0026amp;D. It provides a uniform theoretical framework as a basis for showing how practical planners are developed. Based on the authors' wide experience in teaching and tutorials, the book provides a range of learning paths adaptable to the reader's needs.\" \u003cb\u003e--Austin Tate, AIAI, University of Edinburgh \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e\"\u003c\/b\u003eBy synthesizing a broad range of planning approaches into a common conceptual framework and explaining them with a common set of problems, this book provides unique clarity in understanding these approaches and their interrelationships.\" \u003cb\u003e--David Wilkins, SRI International \u003c\/b\u003e\u003c\/p\u003e\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eAutomated planning technology now plays a significant role in a variety of demanding applications, ranging from controlling space vehicles and robots to playing the game of bridge. These real-world applications create new opportunities for synergy between theory and practice: observing what works well in practice leads to better theories of planning, and better theories lead to better performance of practical applications.\u003c\/p\u003e  \u003cp\u003eAutomated Planning mirrors this dialogue by offering a comprehensive, up-to-date resource on both the theory and practice of automated planning. The book goes well beyond classical planning, to include temporal planning, resource scheduling, planning under uncertainty, and modern techniques for plan generation, such as task decomposition, propositional satisfiability, constraint satisfaction, and model checking.\u003c\/p\u003e  \u003cp\u003eThe authors combine over 30 years experience in planning research and development to offer an invaluable text to researchers, professionals, and graduate students.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e1 Introduction and Overview \u003c\/p\u003e \u003cp\u003e\u003cb\u003eI Classical Planning \u003c\/b\u003e2 Representations for Classical Planning3 Complexity of Classical Planning4 State-Space Planning5 Plan-Space Planning \u003cb\u003eII Neoclassical Planning\u003c\/b\u003e 6 Planning-Graph Techniques7 Propositional Satisfiability Techniques8  Constraint Satisfaction Techniques \u003c\/p\u003e \u003cp\u003e\u003cb\u003eIII Heuristics and Control Strategies \u003c\/b\u003e9 Heuristics in Planning10 Control Rules in Planning11 Hierarchical Task Network Planning12 Control Strategies in Deductive Planning \u003cb\u003eIV Planning with Time and Resources \u003c\/b\u003e13 Time for Planning14 Temporal Planning15 Planning and Resource Scheduling \u003c\/p\u003e \u003cp\u003e\u003cb\u003eV Planning under Uncertainty \u003c\/b\u003e16 Planning based on Markov Decision Processes17 Planning based on Model Checking18 Uncertainty with Neo-Classical Techniques \u003c\/p\u003e \u003cp\u003e\u003cb\u003eVI Case Studies and Applications \u003c\/b\u003e19 Space Applications20 Planning in Robotics21 Planning for Manufacturability Analysis22 Emergency Evacuation Planning 23 Planning in the Game of Bridge\u003c\/p\u003e \u003cp\u003e\u003cb\u003eVII Conclusion \u003c\/b\u003e24 Conclusion and Other Topics \u003c\/p\u003e \u003cp\u003e\u003cb\u003eVIII Appendices \u003c\/b\u003eA Search Procedures and Computational ComplexityB First Order LogicC Model Checking \u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Expert systems \/ knowledge-based systems [\u003ca title=\"See our other books on Expert systems \/ knowledge-based systems\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Expert%20systems%20\/%20knowledge-based%20systems%20%5BUYQE%5D%22\"\u003eUYQE\u003c\/a\u003e], Artificial intelligence [\u003ca title=\"See our other books on Artificial intelligence\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Artificial%20intelligence%20%5BUYQ%5D%22\"\u003eUYQ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Morgan Kaufmann","offers":[{"title":"Default Title","offer_id":46648528371992,"sku":"9781558608566","price":59.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9781558608566.jpg?v=1694807481"},{"product_id":"medical-image-recognition-segmentation-and-parsing-machine-learning-and-multiple-object-approaches-hardback-9780128025819","title":"Medical Image Recognition, Segmentation and Parsing; Machine Learning and Multiple Object Approaches (Hardback) 9780128025819","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eMedical Image Recognition, Segmentation and Parsing\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eMachine Learning and Multiple Object Approaches\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cem\u003e\u003cp\u003eLearn and apply methods and algorithms for automatically recognizing, segmenting and parsing multiple objects\u003c\/p\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eS. Kevin Zhou (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780128025819, Elsevier Science\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 2 December 2015\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e542 pages\u003cbr\u003e23.4 x 19 x 3.1 cm, 1.8 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eThis book describes the technical problems and solutions for automatically recognizing and parsing a medical image into multiple objects, structures, or anatomies. It gives all the key methods, including state-of- the-art approaches based on machine learning, for recognizing or detecting, parsing or segmenting, a cohort of anatomical structures from a medical image.\u003c\/p\u003e \u003cp\u003eWritten by top experts in Medical Imaging, this book is ideal for university researchers and industry practitioners in medical imaging who want a complete reference on key methods, algorithms and applications in medical image recognition, segmentation and parsing of multiple objects.\u003c\/p\u003e  \u003cp\u003eLearn:\u003c\/p\u003e  \u003cul\u003e  \u003cp\u003e \u003c\/p\u003e\n\u003cli\u003eResearch challenges and problems in medical image recognition, segmentation and parsing of multiple objects\u003c\/li\u003e \u003cli\u003eMethods and theories for medical image recognition, segmentation and parsing of multiple objects\u003c\/li\u003e \u003cli\u003eEfficient and effective machine learning solutions based on big datasets\u003c\/li\u003e \u003cli\u003eSelected applications of medical image parsing using proven algorithms\u003c\/li\u003e \u003c\/ul\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePrefaceChapter 1 Introduction to Medical Image Recognition and ParsingChapter 2 Discriminative Anatomy Detection: Classification vs. RegressionChapter 3: Information Theoretic Landmark DetectionChapter 4: Submodular Landmark DetectionChapter 5: Random Forests for Anatomy Recognition Chapter 6: Integrated Detection Network for Multiple Object RecognitionChapter 7: Optimal Graph-Based Method for Multi-Object Segmentation Chapter 8: Parsing of Multiple Organs Using Learning Method and Level SetsChapter 9: Context Integration for Rapid Multiple Organ ParsingChapter 10: Multi-Atlas Methods and Label FusionChapter 11: Multi-Compartment Segmentation Framework Chapter 12: Deformable Segmentation via Sparse Representation and Dictionary Learning Chapter 13: Simultaneous Nonrigid Registration, Segmentation, and Tumor Detection Chapter 14: Whole Brain Anatomical Structure Parsing Chapter 15: Aortic and Mitral Valve Segmentation Chapter 16: Parsing of Heart, Chambers and Coronary Vessels Chapter 17: Spine Segmentation Chapter 18: Parsing of Rib and Knee BonesChapter 19: Lymph Node Segmentation Chapter 20: Polyp Segmentation from CT Colonoscopy\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Image processing [\u003ca title=\"See our other books on Image processing\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Image%20processing%20%5BUYT%5D%22\"\u003eUYT\u003c\/a\u003e], Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Enterprise software [\u003ca title=\"See our other books on Enterprise software\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Enterprise%20software%20%5BUFL%5D%22\"\u003eUFL\u003c\/a\u003e], Medical bioinformatics [\u003ca title=\"See our other books on Medical bioinformatics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Medical%20bioinformatics%20%5BMBF%5D%22\"\u003eMBF\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Academic Press","offers":[{"title":"Default Title","offer_id":46648824758552,"sku":"9780128025819","price":76.59,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780128025819.jpg?v=1694094442"},{"product_id":"tactile-sensing-skill-learning-and-robotic-dexterous-manipulation-paperback-9780323904452","title":"Tactile Sensing, Skill Learning, and Robotic Dexterous Manipulation (Paperback \/ softback) 9780323904452","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eTactile Sensing, Skill Learning, and Robotic Dexterous Manipulation\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003eHelps professionals improve robotic dexterity by skill learning, intelligent perception and adaptive control\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eQiang Li (Edited by), Shan Luo (Edited by), Zhaopeng Chen (Edited by), Chenguang Yang (Edited by), Jianwei Zhang (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780323904452\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback \/ softback, published 7 April 2022\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e372 pages, Approx. 100 illustrations (100 in full color)\u003cbr\u003e22.9 x 15.2 x 2.4 cm, 0.59 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003ci\u003eTactile Sensing, Skill Learning and Robotic Dexterous Manipulation\u003c\/i\u003e focuses on cross-disciplinary lines of research and groundbreaking research ideas in three research lines: tactile sensing, skill learning and dexterous control. The book introduces recent work about human dexterous skill representation and learning, along with discussions of tactile sensing and its applications on unknown objects’ property recognition and reconstruction. Sections also introduce the adaptive control schema and its learning by imitation and exploration. Other chapters describe the fundamental part of relevant research, paying attention to the connection among different fields and showing the state-of-the-art in related branches. \u003c\/p\u003e  \u003cp\u003eThe book summarizes the different approaches and discusses the pros and cons of each. Chapters not only describe the research but also include basic knowledge that can help readers understand the proposed work, making it an excellent resource for researchers and professionals who work in the robotics industry, haptics and in machine learning.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003ePart I: Tactile sensing and perception\u003c\/b\u003e 1. Tactile sensors for dexterous manipulation 2. Robotic perception of object properties using tactile sensing 3. Multimodal perception for dexterous manipulation 4. Using Machine Learning for Material Detection with Capacitive Proximity Sensors\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II: Skill representation and learning\u003c\/b\u003e 5. Admittance control: learning from human and collaboration with human 6. Sensorimotor Control for Dexterous Grasping--Inspiration from human hand 7. Efficient Haptic Learning and Interaction 8. From human to robot grasping: kinematics and forces synergies 9. Learning a form-closure grasping with attractive region in environment 10. Learning hierarchical control for robust in-hand manipulation 11. Learning Industrial Assembly by Guided-DDPG\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III: Robotic hand adaptive control\u003c\/b\u003e 12. The novel poly-articulated prosthetic hand Hannes: A survey study, and clinical evaluation 13. Enhancing vision control by tactile sensing for robotic manipulation 14. Neural Network enhanced Optimal Control of Manipulator 15. Towards Dexterous In-Hand Manipulation of Unknown Objects: A Feedback Based Control Approach 16. Learning Industrial Assembly by Guided-DDPG\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Artificial intelligence [\u003ca title=\"See our other books on Artificial intelligence\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Artificial%20intelligence%20%5BUYQ%5D%22\"\u003eUYQ\u003c\/a\u003e], Robotics [\u003ca title=\"See our other books on Robotics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Robotics%20%5BTJFM1%5D%22\"\u003eTJFM1\u003c\/a\u003e], Electrical engineering [\u003ca title=\"See our other books on Electrical engineering\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Electrical%20engineering%20%5BTHR%5D%22\"\u003eTHR\u003c\/a\u003e], Mechanical engineering [\u003ca title=\"See our other books on Mechanical engineering\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mechanical%20engineering%20%5BTGB%5D%22\"\u003eTGB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Freshly Printed Books","offers":[{"title":"Default Title","offer_id":46648878989592,"sku":"9780323904452","price":100.36,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780323904452_bf1fb61b-2ef8-4d15-ac84-4965560660ed.jpg?v=1694993269"},{"product_id":"artificial-intelligence-for-the-internet-of-everything-paperback-9780128176368","title":"Artificial Intelligence for the Internet of Everything (Paperback) 9780128176368","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eArtificial Intelligence for the Internet of Everything\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cp\u003eComprehensive reference on the interplay between artificial intelligence and the IoT and its effects on sensing, perception, cognition and behavior\u003c\/p\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eWilliam Lawless (Edited by), Ranjeev Mittu (Edited by), Donald Sofge (Edited by), Ira S Moskowitz (Edited by), Stephen Russell (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780128176368\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback, published 25 February 2019\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e303 pages\u003cbr\u003e22.9 x 15.1 x 2 cm, 0.43 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eApprox.291 pages\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e1. Introduction 2. Uncertainty Quantification in Internet of Battlefield Things 3. Intelligent Autonomous Things on the Battlefield 4. Active Inference in Multi-agent Systems: Context-driven Collaboration and Decentralized Purpose-driven Team Adaptation 5. Policy Issues Regarding Implementations of Cyber Attack. Resilience Solutions for Cyber Physical Systems 6. Trust and Human-Machine Teaming: A Qualitative Study 7. The Web of Smart Entities – Aspects of a Theory of the Next Generation of the Internet of Things 8. Raising Them Right: AI and the Internet of Big Things 9. Valuable Information and the Internet of Things 10. Would IOET Make Economics More Neoclassical or More Behavioral? Richard Thaler’s Prediction, A Revisit 11. Accessing Validity of Argumentation of Agents of the Internet of Everything 12. Distributed Autonomous Energy Organizations: Next Generation Blockchain Applications for Energy Infrastructure 13. Compositional Models for Complex Systems 14. Meta-agents: Using Multi-Agent Networks to Manage Dynamic Changes in the Internet of Things (IoT)\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Expert systems \/ knowledge-based systems [\u003ca title=\"See our other books on Expert systems \/ knowledge-based systems\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Expert%20systems%20\/%20knowledge-based%20systems%20%5BUYQE%5D%22\"\u003eUYQE\u003c\/a\u003e], Artificial intelligence [\u003ca title=\"See our other books on Artificial intelligence\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Artificial%20intelligence%20%5BUYQ%5D%22\"\u003eUYQ\u003c\/a\u003e], Databases \u0026amp; the Web [\u003ca title=\"See our other books on Databases \u0026amp; the Web\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Databases%20\u0026amp;%20the%20Web%20%5BUNN%5D%22\"\u003eUNN\u003c\/a\u003e], Enterprise software [\u003ca title=\"See our other books on Enterprise software\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Enterprise%20software%20%5BUFL%5D%22\"\u003eUFL\u003c\/a\u003e], Internet: general works [\u003ca title=\"See our other books on Internet: general works\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Internet:%20general%20works%20%20%5BUBW%5D%22\"\u003eUBW\u003c\/a\u003e], Clinical psychology [\u003ca title=\"See our other books on Clinical psychology\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Clinical%20psychology%20%5BMMJ%5D%22\"\u003eMMJ\u003c\/a\u003e], Geopolitics [\u003ca title=\"See our other books on Geopolitics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Geopolitics%20%5BJPSL%5D%22\"\u003eJPSL\u003c\/a\u003e], Cognition \u0026amp; cognitive psychology [\u003ca title=\"See our other books on Cognition \u0026amp; cognitive psychology\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Cognition%20\u0026amp;%20cognitive%20psychology%20%5BJMR%5D%22\"\u003eJMR\u003c\/a\u003e], Computational linguistics [\u003ca title=\"See our other books on Computational linguistics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computational%20linguistics%20%5BCFX%5D%22\"\u003eCFX\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Freshly Printed Books","offers":[{"title":"Default Title","offer_id":46648972804376,"sku":"9780128176368","price":86.29,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780128176368.jpg?v=1694095797"},{"product_id":"machine-learning-and-data-mining-paperback-9781904275213","title":"Machine Learning and Data Mining (Paperback \/ softback) 9781904275213","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eMachine Learning and Data Mining\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eIgor Kononenko (Author), Matjaz Kukar (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781904275213, Elsevier Science\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback \/ softback, published 30 April 2007\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e480 pages\u003cbr\u003e23.3 x 15.6 x 3 cm, 0.71 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e\"Readers are treated to a comprehensive look at the principles. …a fine overview of machine learning methods. …Recommended.\" \u003cb\u003e--Choice Magazine\u003c\/b\u003e\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eData mining is often referred to by real-time users and software solutions providers as knowledge discovery in databases (KDD). Good data mining practice for business intelligence (the art of turning raw software into meaningful information) is demonstrated by the many new techniques and developments in the conversion of fresh scientific discovery into widely accessible software solutions. This book has been written as an introduction to the main issues associated with the basics of machine learning and the algorithms used in data mining.Suitable for advanced undergraduates and their tutors at postgraduate level in a wide area of computer science and technology topics as well as researchers looking to adapt various algorithms for particular data mining tasks. A valuable addition to the libraries and bookshelves of the many companies who are using the principles of data mining (or KDD) to effectively deliver solid business and industry solutions.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cul\u003e \u003cli\u003eForeword\u003c\/li\u003e \u003cli\u003ePreface  \u003cul\u003e \u003cli\u003eAcknowledgements\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e \u003cli\u003eChapter 1: Introduction  \u003cul\u003e \u003cli\u003e1.1 THE NAME OF THE GAME\u003c\/li\u003e \u003cli\u003e1.2 OVERVIEW OF MACHINE LEARNING METHODS\u003c\/li\u003e \u003cli\u003e1.3 HISTORY OF MACHINE LEARNING\u003c\/li\u003e \u003cli\u003e1.4 SOME EARLY SUCCESSES\u003c\/li\u003e \u003cli\u003e1.5 APPLICATIONS OF MACHINE LEARNING\u003c\/li\u003e \u003cli\u003e1.6 DATA MINING TOOLS AND STANDARDS\u003c\/li\u003e \u003cli\u003e1.7 SUMMARY AND FURTHER READING\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e \u003cli\u003eChapter 2: Learning and Intelligence  \u003cul\u003e \u003cli\u003e2.1 WHAT IS LEARNING\u003c\/li\u003e \u003cli\u003e2.2 NATURAL LEARNING\u003c\/li\u003e \u003cli\u003e2.3 LEARNING, INTELLIGENCE, CONSCIOUSNESS\u003c\/li\u003e \u003cli\u003e2.4 WHY MACHINE LEARNING\u003c\/li\u003e \u003cli\u003e2.5 SUMMARY AND FURTHER READING\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e \u003cli\u003eChapter 3: Machine Learning Basics  \u003cul\u003e \u003cli\u003e3.1 BASIC PRINCIPLES\u003c\/li\u003e \u003cli\u003e3.2 MEASURES FOR PERFORMANCE EVALUATION\u003c\/li\u003e \u003cli\u003e3.3 ESTIMATING PERFORMANCE\u003c\/li\u003e \u003cli\u003e3.4 *COMPARING PERFORMANCE OF MACHINE LEARNING ALGORITHMS\u003c\/li\u003e \u003cli\u003e3.5 COMBINING SEVERAL MACHINE LEARNING ALGORITHMS\u003c\/li\u003e \u003cli\u003e3.6 SUMMARY AND FURTHER READING\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e \u003cli\u003eChapter 4: Knowledge Representation  \u003cul\u003e \u003cli\u003e4.1 PROPOSITIONAL CALCULUS\u003c\/li\u003e \u003cli\u003e4.2 *FIRST ORDER PREDICATE CALCULUS\u003c\/li\u003e \u003cli\u003e4.3 DISCRIMINANT AND REGRESSION FUNCTIONS\u003c\/li\u003e \u003cli\u003e4.4 PROBABILITY DISTRIBUTIONS\u003c\/li\u003e \u003cli\u003e4.5 SUMMARY AND FURTHER READING\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e \u003cli\u003eChapter 5: Learning as Search  \u003cul\u003e \u003cli\u003e5.1 EXHAUSTIVE SEARCH\u003c\/li\u003e \u003cli\u003e5.2 BOUNDED EXHAUSTIVE SEARCH (BRANCH AND BOUND)\u003c\/li\u003e \u003cli\u003e5.3 BEST-FIRST SEARCH\u003c\/li\u003e \u003cli\u003e5.4 GREEDY SEARCH\u003c\/li\u003e \u003cli\u003e5.5 BEAM SEARCH\u003c\/li\u003e \u003cli\u003e5.6 LOCAL OPTIMIZATION\u003c\/li\u003e \u003cli\u003e5.7 GRADIENT SEARCH\u003c\/li\u003e \u003cli\u003e5.8 SIMULATED ANNEALING\u003c\/li\u003e \u003cli\u003e5.9 GENETIC ALGORITHMS\u003c\/li\u003e \u003cli\u003e5.10 SUMMARY AND FURTHER READING\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e \u003cli\u003eChapter 6: Measures for Evaluating the Quality of Attributes  \u003cul\u003e \u003cli\u003e6.1 MEASURES FOR CLASSIFICATION AND RELATIONAL PROBLEMS\u003c\/li\u003e \u003cli\u003e6.2 MEASURES FOR REGRESSION\u003c\/li\u003e \u003cli\u003e6.3 **FORMAL DERIVATIONS AND PROOFS\u003c\/li\u003e \u003cli\u003e6.4 SUMMARY AND FURTHER READING\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e \u003cli\u003eChapter 7: Data Preprocessing  \u003cul\u003e \u003cli\u003e7.1 REPRESENTATION OF COMPLEX STRUCTURES\u003c\/li\u003e \u003cli\u003e7.2 DISCRETIZATION OF CONTINUOUS ATTRIBUTES\u003c\/li\u003e \u003cli\u003e7.3 ATTRIBUTE BINARIZATION\u003c\/li\u003e \u003cli\u003e7.4 TRANSFORMING DISCRETE ATTRIBUTES INTO CONTINUOUS\u003c\/li\u003e \u003cli\u003e7.5 DEALING WITH MISSING VALUES\u003c\/li\u003e \u003cli\u003e7.6 VISUALIZATION\u003c\/li\u003e \u003cli\u003e7.7 DIMENSIONALITY REDUCTION\u003c\/li\u003e \u003cli\u003e7.8 **FORMAL DERIVATIONS AND PROOFS\u003c\/li\u003e \u003cli\u003e7.9 SUMMARY AND FURTHER READING\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e \u003cli\u003eChapter 8: *Constructive Induction  \u003cul\u003e \u003cli\u003e8.1 DEPENDENCE OF ATTRIBUTES\u003c\/li\u003e \u003cli\u003e8.2 CONSTRUCTIVE INDUCTION WITH PRE-DEFINED OPERATORS\u003c\/li\u003e \u003cli\u003e8.3 CONSTRUCTIVE INDUCTION WITHOUT PRE-DEFINED OPERATORS\u003c\/li\u003e \u003cli\u003e8.4 SUMMARY AND FURTHER READING\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e \u003cli\u003eChapter 9: Symbolic Learning  \u003cul\u003e \u003cli\u003e9.1 LEARNING OF DECISION TREES\u003c\/li\u003e \u003cli\u003e9.2 LEARNING OF DECISION RULES\u003c\/li\u003e \u003cli\u003e9.3 LEARNING OF ASSOCIATION RULES\u003c\/li\u003e \u003cli\u003e9.4 LEARNING OF REGRESSION TREES\u003c\/li\u003e \u003cli\u003e9.5 *INDUCTIVE LOGIC PROGRAMMING\u003c\/li\u003e \u003cli\u003e9.6 NAIVE AND SEMI-NAIVE BAYESIAN CLASSIFIER\u003c\/li\u003e \u003cli\u003e9.7 BAYESIAN BELIEF NETWORKS\u003c\/li\u003e \u003cli\u003e9.8 SUMMARY AND FURTHER READING\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e \u003cli\u003eChapter 10: Statistical Learning  \u003cul\u003e \u003cli\u003e10.1 NEAREST NEIGHBORS\u003c\/li\u003e \u003cli\u003e10.2 DISCRIMINANT ANALYSIS\u003c\/li\u003e \u003cli\u003e10.3 LINEAR REGRESSION\u003c\/li\u003e \u003cli\u003e10.4 LOGISTIC REGRESSION\u003c\/li\u003e \u003cli\u003e10.5 *SUPPORT VECTOR MACHINES\u003c\/li\u003e \u003cli\u003e10.6 SUMMARY AND FURTHER READING\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e \u003cli\u003eChapter 11: Artificial Neural Networks  \u003cul\u003e \u003cli\u003e11.1 INTRODUCTION\u003c\/li\u003e \u003cli\u003e11.2 TYPES OF ARTIFICIAL NEURAL NETWORKS\u003c\/li\u003e \u003cli\u003e11.3 *HOPFIELD’S NEURAL NETWORK\u003c\/li\u003e \u003cli\u003e11.4 *BAYESIAN NEURAL NETWORK\u003c\/li\u003e \u003cli\u003e11.5 PERCEPTRON\u003c\/li\u003e \u003cli\u003e11.6 RADIAL BASIS FUNCTION NETWORKS\u003c\/li\u003e \u003cli\u003e11.7 **FORMAL DERIVATIONS AND PROOFS\u003c\/li\u003e \u003cli\u003e11.8 SUMMARY AND FURTHER READING\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e \u003cli\u003eChapter 12: Cluster Analysis  \u003cul\u003e \u003cli\u003e12.1 INTRODUCTION\u003c\/li\u003e \u003cli\u003e12.2 MEASURES OF DISSIMILARITY\u003c\/li\u003e \u003cli\u003e12.3 HIERARCHICAL CLUSTERING\u003c\/li\u003e \u003cli\u003e12.4 PARTITIONAL CLUSTERING\u003c\/li\u003e \u003cli\u003e12.5 MODEL-BASED CLUSTERING\u003c\/li\u003e \u003cli\u003e12.6 OTHER CLUSTERING METHODS\u003c\/li\u003e \u003cli\u003e12.7 SUMMARY AND FURTHER READING\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e \u003cli\u003eChapter 13: **Learning Theory  \u003cul\u003e \u003cli\u003e13.1 COMPUTABILITY THEORY AND RECURSIVE FUNCTIONS\u003c\/li\u003e \u003cli\u003e13.2 FORMAL LEARNING THEORY\u003c\/li\u003e \u003cli\u003e13.3 PROPERTIES OF LEARNING FUNCTIONS\u003c\/li\u003e \u003cli\u003e13.4 PROPERTIES OF INPUT DATA\u003c\/li\u003e \u003cli\u003e13.5 CONVERGENCE CRITERIA\u003c\/li\u003e \u003cli\u003e13.6 IMPLICATIONS FOR MACHINE LEARNING\u003c\/li\u003e \u003cli\u003e13.7 SUMMARY AND FURTHER READING\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e \u003cli\u003eChapter 14: **Computational Learning Theory  \u003cul\u003e \u003cli\u003e14.1 INTRODUCTION\u003c\/li\u003e \u003cli\u003e14.2 GENERAL FRAMEWORK FOR CONCEPT LEARNING\u003c\/li\u003e \u003cli\u003e14.3 PAC LEARNING MODEL\u003c\/li\u003e \u003cli\u003e14.4 VAPNIK-CHERVONENKIS DIMENSION\u003c\/li\u003e \u003cli\u003e14.5 LEARNING IN THE PRESENCE OF NOISE\u003c\/li\u003e \u003cli\u003e14.6 EXACT AND MISTAKE BOUNDED LEARNING MODELS\u003c\/li\u003e \u003cli\u003e14.7 INHERENT UNPREDICTABILITY AND PAC-REDUCTIONS\u003c\/li\u003e \u003cli\u003e14.8 WEAK AND STRONG LEARNING\u003c\/li\u003e \u003cli\u003e14.9 SUMMARY AND FURTHER READING\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e \u003cli\u003eAppendix A: *Definitions of some lesser known terms  \u003cul\u003e \u003cli\u003eA.1 COMPUTATIONAL COMPLEXITY CLASSES\u003c\/li\u003e \u003cli\u003eA.2 ASYMPTOTIC NOTATION\u003c\/li\u003e \u003cli\u003eA.3 SOME BOUNDS FOR PROBABILISTIC ANALYSIS\u003c\/li\u003e \u003cli\u003eA.4 COVARIANCE MATRIX\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e \u003cli\u003eReferences\u003c\/li\u003e \u003cli\u003eIndex\u003c\/li\u003e\n\u003c\/ul\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Databases [\u003ca title=\"See our other books on Databases\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Databases%20%5BUN%5D%22\"\u003eUN\u003c\/a\u003e], Library, archive \u0026amp; information management [\u003ca title=\"See our other books on Library, archive \u0026amp; information management\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Library,%20archive%20\u0026amp;%20information%20management%20%5BGLC%5D%22\"\u003eGLC\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Woodhead Publishing","offers":[{"title":"Default Title","offer_id":46648992661784,"sku":"9781904275213","price":64.79,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9781904275213_929586cb-1e55-404c-adf7-9a1669e193f1.jpg?v=1696598735"},{"product_id":"trends-in-deep-learning-methodologies-algorithms-applications-and-systems-paperback-9780128222263","title":"Trends in Deep Learning Methodologies; Algorithms, Applications, and Systems (Paperback) 9780128222263","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eTrends in Deep Learning Methodologies\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eAlgorithms, Applications, and Systems\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cem\u003ePresents the latest advanced research in computational intelligence and deep learning mechanisms and applications\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eVincenzo Piuri (Edited by), Sandeep Raj (Edited by), Angelo Genovese (Edited by), Rajshree Srivastava (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780128222263\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback, published 16 November 2020\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e306 pages\u003cbr\u003e22.9 x 15.1 x 2 cm, 0.48 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003ci\u003eTrends in Deep Learning Methodologies: Algorithms, Applications, and Systems\u003c\/i\u003e covers deep learning approaches such as neural networks, deep belief networks, recurrent neural networks, convolutional neural networks, deep auto-encoder, and deep generative networks, which have emerged as powerful computational models. Chapters elaborate on these models which have shown significant success in dealing with massive data for a large number of applications, given their capacity to extract complex hidden features and learn efficient representation in unsupervised settings. Chapters investigate deep learning-based algorithms in a variety of application, including biomedical and health informatics, computer vision, image processing, and more. \u003c\/p\u003e  \u003cp\u003eIn recent years, many powerful algorithms have been developed for matching patterns in data and making predictions about future events. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. Deep learning methods can deal with multiple levels of representation in which the system learns to abstract higher level representations of raw data. Earlier, it was a common requirement to have a domain expert to develop a specific model for each specific application, however, recent advancements in representation learning algorithms allow researchers across various subject domains to automatically learn the patterns and representation of the given data for the development of specific models.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e1. An Introduction\/ theoretical understanding to deep learning – challenges, feasibility in domains  2. Deep learning for big data 3. Deep learning in signal processing 4. Deep learning in image processing 5. Deep learning in video processing 6. Deep learning in audio\/speech processing 7. Deep learning in data mining 8. Deep learning in healthcare 9. Deep learning in biomedical research 10. Deep learning in agriculture 11. Deep learning in environmental sciences 12. Deep learning in economics\/e-commerce 13. Deep learning in forensics (biometrics recognition) 14. Deep learning in cybersecurity 15. Deep learning for smart cities, smart hospitals, and smart homes\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Freshly Printed Books","offers":[{"title":"Default Title","offer_id":46649079333144,"sku":"9780128222263","price":102.79,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780128222263.jpg?v=1694097062"},{"product_id":"data-mining-know-it-all-hardback-9780123746290","title":"Data Mining: Know It All (Hardback) 9780123746290","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eData Mining: Know It All\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003e\u003ci\u003eAll of the elements of data mining together in a single volume written by the best and brightest experts in the field!\u003c\/i\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eSoumen Chakrabarti (Author), Richard E. Neapolitan (Author), Dorian Pyle (Author), Mamdouh Refaat (Author), Markus Schneider (Author), Toby J. Teorey (Author), Ian H. Witten (Author), Earl Cox (Author), Eibe Frank (Author), Ralf Hartmut Güting (Author), Jiawei Han (Author), Xia Jiang (Author), Micheline Kamber (Author), Sam S. Lightstone (Author), Thomas P. Nadeau (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780123746290, Elsevier Science\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 27 November 2008\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e480 pages\u003cbr\u003e23.4 x 19 x 2.9 cm, 1.14 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eThis book brings all of the elements of data mining together in a single volume, saving the reader the time and expense of making multiple purchases. It consolidates both introductory and advanced topics, thereby covering the gamut of data mining and machine learning tactics ? from data integration and pre-processing, to fundamental algorithms, to optimization techniques and web mining methodology. The proposed book expertly combines the finest data mining material from the Morgan Kaufmann portfolio. Individual chapters are derived from a select group of MK books authored by the best and brightest in the field. These chapters are combined into one comprehensive volume in a way that allows it to be used as a reference work for those interested in new and developing aspects of data mining. This book represents a quick and efficient way to unite valuable content from leading data mining experts, thereby creating a definitive, one-stop-shopping opportunity for customers to receive the information they would otherwise need to round up from separate sources.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eChapter 1: Data Mining Overview Chapter 2: Data Acquisition and Integration Chapter 3: Data Pre-processing  Chapter 4: Physical Design for Decision Support, Warehousing, and OLAPChapter 5: Algorithms - The Basic Methods Chapter 6: Further Techniques in Decision Analysis Chapter 7: Fundamental Concepts of Genetic Algorithms Chapter 8: Spatio-Temporal Data Structures and Algorithms for Moving Objects Types Chapter 9: Improving the Mined ModelChapter 10: Web Mining - Social Network Analysis\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Data mining [\u003ca title=\"See our other books on Data mining\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Data%20mining%20%5BUNF%5D%22\"\u003eUNF\u003c\/a\u003e], Library, archive \u0026amp; information management [\u003ca title=\"See our other books on Library, archive \u0026amp; information management\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Library,%20archive%20\u0026amp;%20information%20management%20%5BGLC%5D%22\"\u003eGLC\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Morgan Kaufmann","offers":[{"title":"Default Title","offer_id":46649297436952,"sku":"9780123746290","price":49.69,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780123746290.jpg?v=1695013933"},{"product_id":"data-mining-and-knowledge-discovery-for-geoscientists-hardback-9780124104372","title":"Data Mining and Knowledge Discovery for Geoscientists (Hardback) 9780124104372","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eData Mining and Knowledge Discovery for Geoscientists\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cp\u003eAuthored by a global thought leader in data mining, this book summarizes the latest developments and practical data application techniques for geoscientists. \u003c\/p\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eGuangren Shi (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780124104372\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 12 December 2013\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e376 pages\u003cbr\u003e23.4 x 19 x 2.6 cm, 0.96 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\"Shi introduces geological scientists to algorithms that are widely used for data mining and knowledge discovery, describes how they have been and could be applied in the geosciences, and surveys some successful applications. The algorithms fall into the categories of probability and statistics, artificial neural networks, support vector machines, decision trees, Bayesian classification, cluster analysis, the Kriging method, and fuzzy mathematics…\"-\u003cb\u003eProtoView.com, February 2014\u003c\/b\u003e\u003c\/p\u003e\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eCurrently there are major challenges in data mining applications in the geosciences. This is due primarily to the fact that there is a wealth of available mining data amid an absence of the knowledge and expertise necessary to analyze and accurately interpret the same data. Most geoscientists have no practical knowledge or experience using data mining techniques. For the few that do, they typically lack expertise in using data mining software and in selecting the most appropriate algorithms for a given application. This leads to a paradoxical scenario of \"rich data but poor knowledge\". \u003c\/p\u003e  \u003cp\u003eThe true solution is to apply data mining techniques in geosciences databases and to modify these techniques for practical applications. Authored by a global thought leader in data mining, \u003ci\u003eData Mining and Knowledge Discovery for Geoscientists\u003c\/i\u003e addresses these challenges by summarizing the latest developments in geosciences data mining and arming scientists with the ability to apply key concepts to effectively analyze and interpret vast amounts of critical information.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eIntroduction 1 Introduction to Data Mining2 Probability and Statistics 3 Artificial Neural Networks 4 Support Vector Machines5 Decision Trees (DTR)6 Bayesian Classification7 Cluster Analysis 8 Kriging Method 9 Other Soft Computing Methods for the Geosciences 10 A Practical Data Mining and Knowledge Discovery System for the GeosciencesIndex\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Data mining [\u003ca title=\"See our other books on Data mining\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Data%20mining%20%5BUNF%5D%22\"\u003eUNF\u003c\/a\u003e], Geology \u0026amp; the lithosphere [\u003ca title=\"See our other books on Geology \u0026amp; the lithosphere\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Geology%20\u0026amp;%20the%20lithosphere%20%5BRBG%5D%22\"\u003eRBG\u003c\/a\u003e], Earth sciences [\u003ca title=\"See our other books on Earth sciences\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Earth%20sciences%20%5BRB%5D%22\"\u003eRB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Freshly Printed Books","offers":[{"title":"Default Title","offer_id":46649401540888,"sku":"9780124104372","price":82.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780124104372.jpg?v=1694100764"},{"product_id":"industrial-agents-emerging-applications-of-software-agents-in-industry-paperback-9780128003411","title":"Industrial Agents; Emerging Applications of Software Agents in Industry (Paperback) 9780128003411","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eIndustrial Agents\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eEmerging Applications of Software Agents in Industry\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cem\u003eAn edited work presenting the latest research and industry applications of agent-based software systems\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003ePaulo Leitão (Edited by), Stamatis Karnouskos (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780128003411, Elsevier Science\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback, published 13 March 2015\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e476 pages\u003cbr\u003e23.4 x 19 x 3 cm, 1 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e\"...provides an extended survey of the field, contrasting, comparing, and citing every notable research effort that has contributed to the current state of the art...a summary of phase one, and the launching platform for stage two in industrial agent development.\" \u003cbr\u003e\u003cb\u003e-- Computing Reviews\u003c\/b\u003e\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003ci\u003eIndustrial Agents \u003c\/i\u003eexplains how multi-agent systems improve collaborative networks to offer dynamic service changes, customization, improved quality and reliability, and flexible infrastructure. Learn how these platforms can offer distributed intelligent management and control functions with communication, cooperation and synchronization capabilities, and also provide for the behavior specifications of the smart components of the system. The book offers not only an introduction to industrial agents, but also clarifies and positions the vision, on-going efforts, example applications, assessment and roadmap applicable to multiple industries. This edited work is guided and co-authored by leaders of the IEEE Technical Committee on Industrial Agents who represent both academic and industry perspectives and share the latest research along with their hands-on experiences prototyping and deploying industrial agents in industrial scenarios.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e1. Definition of Industrial Agents\u003cbr\u003e2. Requirements\u003cbr\u003e3. Methodologies and technologies\u003cbr\u003e4. Ontologies\u003cbr\u003e5. Service-oriented architectures and Web services\u003cbr\u003e6. Low-level control\u003cbr\u003e7. Simulation\u003cbr\u003e8. Standardization\u003cbr\u003e9. Benchmarking\u003cbr\u003e10. Emergent topics (self-organization)\u003cbr\u003e11. Example Applications\u003cbr\u003e12. Vision\u003cbr\u003e13. Road-blockers\u003cbr\u003e14. Roadmap\u003cbr\u003e15. Markets\/ application domains\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Artificial intelligence [\u003ca title=\"See our other books on Artificial intelligence\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Artificial%20intelligence%20%5BUYQ%5D%22\"\u003eUYQ\u003c\/a\u003e], Computer programming \/ software development [\u003ca title=\"See our other books on Computer programming \/ software development\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20programming%20\/%20software%20development%20%5BUM%5D%22\"\u003eUM\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Elsevier","offers":[{"title":"Default Title","offer_id":46649445318936,"sku":"9780128003411","price":72.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780128003411.jpg?v=1694101011"},{"product_id":"introduction-to-statistical-machine-learning-paperback-9780128021217","title":"Introduction to Statistical Machine Learning (Paperback) 9780128021217","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eIntroduction to Statistical Machine Learning\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cp\u003eBridges the gap between theory and practice by providing a general introduction to machine learning that covers a wide range of topics concisely\u003c\/p\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eMasashi Sugiyama (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780128021217, Elsevier Science\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback, published 28 September 2015\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e534 pages\u003cbr\u003e23.4 x 19 x 3.3 cm, 1.11 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e\"The probabilistic and statistical background is well presented, providing the reader with a complete coverage of the generative approach to statistical pattern recognition and the discriminative approach to statistical machine learning.\" --\u003cb\u003eZentralblatt MATH\u003c\/b\u003e\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eMachine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science\/engineering areas including, image processing, speech processing, natural language processing, robot control, as well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials. \u003c\/p\u003e\n\u003ci\u003e  \u003c\/i\u003e\u003cp\u003eIntroduction to Statistical Machine Learning provides a\u003ci\u003e \u003c\/i\u003egeneral introduction to machine learning that covers a wide range of topics concisely and will help you bridge the gap between theory and practice. Part I discusses the fundamental concepts of statistics and probability that are used in describing machine learning algorithms. Part II and Part III explain the two major approaches of machine learning techniques; generative methods and discriminative methods. While Part III provides an in-depth look at advanced topics that play essential roles in making machine learning algorithms more useful in practice. The accompanying MATLAB\/Octave programs provide you with the necessary practical skills needed to accomplish a wide range of data analysis tasks.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003ePart I: Introduction to Statistics and Probability\u003c\/b\u003e1. Random variables and probability distributions2. Examples of discrete probability distributions3. Examples of continuous probability distributions4. Multi-dimensional probability distributions5. Examples of multi-dimensional probability distributions6. Random sample generation from arbitrary probability distributions7. Probability distributions of the sum of independent random variables8. Probability inequalities9. Statistical inference10. Hypothesis testing\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II: Generative Approach to Statistical Pattern Recognition\u003c\/b\u003e11. Fundamentals of statistical pattern recognition12. Criteria for developing classifiers13. Maximum likelihood estimation14. Theoretical properties of maximum likelihood estimation15. Linear discriminant analysis16. Model selection for maximum likelihood estimation17. Maximum likelihood estimation for Gaussian mixture model18. Bayesian inference19. Numerical computation in Bayesian inference20. Model selection in Bayesian inference21. Kernel density estimation22. Nearest neighbor density estimation\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III: Discriminative Approach to Statistical Machine Learning\u003c\/b\u003e23. Fundamentals of statistical machine learning24. Learning Models25. Least-squares regression26. Constrained least-squares regression27. Sparse regression28. Robust regression29. Least-squares classification30. Support vector classification31. Ensemble classification32. Probabilistic classification33. Structured classification\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV: Further Topics\u003c\/b\u003e34. Outlier detection35. Unsupervised dimensionality reduction36. Clustering37. Online learning38. Semi-supervised learning39. Supervised dimensionality reduction40. Transfer learning41. 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Theory, Algorithms and Applications (Paperback) 9780128027677","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eHidden Semi-Markov Models\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eTheory, Algorithms and Applications\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cem\u003e\u003cp\u003eThe latest information, new developments and emerging topics about HSMMs, including illustrated examples, with a more in-depth treatment and foundational approach in the understanding and application of HSMMs.\u003c\/p\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eShun-Zheng Yu (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780128027677, Elsevier Science\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback, published 27 October 2015\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e208 pages\u003cbr\u003e22.9 x 15.1 x 1.4 cm, 0.39 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e\"This book is intended to present theory, models, methods, and applications regarding hidden semi-Markov models...It also provides the latest development and emerging topics concerning this field.\" --\u003cb\u003eZentralblatt MATH\u003c\/b\u003e\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eHidden semi-Markov models (HSMMs) are among the most important models in the area of artificial intelligence \/ machine learning. Since the first HSMM was introduced in 1980 for machine recognition of speech, three other HSMMs have been proposed, with various definitions of duration and observation distributions. Those models have different expressions, algorithms, computational complexities, and applicable areas, without explicitly interchangeable forms. \u003c\/p\u003e\n\u003cb\u003e \u003c\/b\u003e  \u003cp\u003e\u003ci\u003eHidden Semi-Markov Models: Theory, Algorithms and Applications\u003c\/i\u003e provides a unified and foundational approach to HSMMs, including various HSMMs (such as the explicit duration, variable transition, and residential time of HSMMs), inference and estimation algorithms, implementation methods and application instances. Learn new developments and state-of-the-art emerging topics as they relate to HSMMs, presented with examples drawn from medicine, engineering and computer science.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e1. Introduction2. Inference of General Hidden Semi-Markov Model3. Estimation of General Hidden Semi-Markov Model4. Implementation of the Algorithms5. Conventional Models6. Various Duration Distributions8. Variants of HSMM9. Applications of HSMM\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Expert systems \/ knowledge-based systems [\u003ca title=\"See our other books on Expert systems \/ knowledge-based systems\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Expert%20systems%20\/%20knowledge-based%20systems%20%5BUYQE%5D%22\"\u003eUYQE\u003c\/a\u003e], Artificial intelligence [\u003ca title=\"See our other books on Artificial intelligence\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Artificial%20intelligence%20%5BUYQ%5D%22\"\u003eUYQ\u003c\/a\u003e], Probability \u0026amp; statistics [\u003ca title=\"See our other books on Probability \u0026amp; statistics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Probability%20\u0026amp;%20statistics%20%5BPBT%5D%22\"\u003ePBT\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Elsevier","offers":[{"title":"Default Title","offer_id":46649592840472,"sku":"9780128027677","price":24.39,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780128027677.jpg?v=1694102066"},{"product_id":"advances-in-independent-component-analysis-and-learning-machines-hardback-9780128028063","title":"Advances in Independent Component Analysis and Learning Machines (Hardback) 9780128028063","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eAdvances in Independent Component Analysis and Learning Machines\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cp\u003eThe very latest advances in independent component analysis and machine learning\u003c\/p\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eElla Bingham (Edited by), Samuel Kaski (Edited by), Jorma Laaksonen (Edited by), Jouko Lampinen (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780128028063\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 15 April 2015\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e328 pages\u003cbr\u003e23.4 x 19 x 2.4 cm, 0.75 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eApprox.296 pages\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003ePart 1: Methods \u003c\/b\u003e1. The Initial Convergence Rate of the FastICA Algorithm: The \"One-Third Rule\" 2. Improved variants of the FastICA algorithm 3. A unified probabilistic model for independent and principal component analysis 4. Riemannian optimization in complex-valued ICA 5. Non-Additive Optimization 6. Image denoising via local factor analysis under Bayesian Ying-Yang principle 7. Unsupervised Deep Learning: A Short Review 8. From Neural PCA to Deep Unsupervised Learning\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 2: Applications \u003c\/b\u003e9. Two Decades of Local Binary Patterns – A Survey 10. Subspace approach in Spectral Color Science 11. From pattern recognition methods to machine vision applications 12. Advances in Visual Concept Detection: Ten Years of TRECVID 13. On the applicability of latent variable modeling to research system data\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Signal processing [\u003ca title=\"See our other books on Signal processing\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Signal%20processing%20%5BUYS%5D%22\"\u003eUYS\u003c\/a\u003e], Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Stochastics [\u003ca title=\"See our other books on Stochastics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Stochastics%20%5BPBWL%5D%22\"\u003ePBWL\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Freshly Printed Books","offers":[{"title":"Default Title","offer_id":46649595035928,"sku":"9780128028063","price":101.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780128028063.jpg?v=1694102077"},{"product_id":"a-machine-learning-approach-to-phishing-detection-and-defense-paperback-9780128029275","title":"A Machine-Learning Approach to Phishing Detection and Defense (Paperback) 9780128029275","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eA Machine-Learning Approach to Phishing Detection and Defense\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eO.A. Akanbi (Author), Iraj Sadegh Amiri (Author), E. Fazeldehkordi (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780128029275, Elsevier Science\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback, published 8 December 2014\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e100 pages, 10 illustrations\u003cbr\u003e22.9 x 15.2 x 0.8 cm, 0.16 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003ePhishing is one of the most widely-perpetrated forms of cyber attack, used to gather sensitive information such as credit card numbers, bank account numbers, and user logins and passwords, as well as other information entered via a web site. The authors of \u003cb\u003e\u003ci\u003eA Machine-Learning Approach to Phishing Detetion and Defense\u003c\/i\u003e\u003c\/b\u003e have conducted research to demonstrate how a machine learning algorithm can be used as an effective and efficient tool in detecting phishing websites and designating them as information security threats. This methodology can prove useful to a wide variety of businesses and organizations who are seeking solutions to this long-standing threat. \u003cb\u003e\u003ci\u003eA Machine-Learning Approach to Phishing Detetion and Defense\u003c\/i\u003e\u003c\/b\u003e also provides information security researchers with a starting point for leveraging the machine algorithm approach as a solution to other information security threats.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003col\u003e \u003cli\u003eIntroduction\u003c\/li\u003e \u003cli\u003eLiterature Review\u003c\/li\u003e \u003cli\u003eResearch Methodology\u003c\/li\u003e \u003cli\u003eFeature Extraction\u003c\/li\u003e \u003cli\u003eImplementation and Result\u003c\/li\u003e \u003cli\u003eConclusions\u003c\/li\u003e\n\u003c\/ol\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Computer security [\u003ca title=\"See our other books on Computer security\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20security%20%5BUR%5D%22\"\u003eUR\u003c\/a\u003e], Databases \u0026amp; the Web [\u003ca title=\"See our other books on Databases \u0026amp; the Web\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Databases%20\u0026amp;%20the%20Web%20%5BUNN%5D%22\"\u003eUNN\u003c\/a\u003e], Internet: general works [\u003ca title=\"See our other books on Internet: general works\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Internet:%20general%20works%20%20%5BUBW%5D%22\"\u003eUBW\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Syngress","offers":[{"title":"Default Title","offer_id":46649595560216,"sku":"9780128029275","price":44.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780128029275.jpg?v=1694102081"},{"product_id":"learning-based-adaptive-control-an-extremum-seeking-approach-theory-and-applications-paperback-9780128031360","title":"Learning-Based Adaptive Control; An Extremum Seeking Approach – Theory and Applications (Paperback \/ softback) 9780128031360","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eLearning-Based Adaptive Control\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eAn Extremum Seeking Approach – Theory and Applications\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cem\u003e\u003cp\u003ePresents comprehensive information on Adaptive Control for optimal action based on the current characteristics of a system\u003c\/p\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eMouhacine Benosman (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780128031360\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback \/ softback, published 11 July 2016\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e282 pages\u003cbr\u003e22.9 x 15.1 x 1.9 cm, 0.4 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eAdaptive control has been one of the main problems studied in control theory. The subject is well understood, yet it has a very active research frontier. This book focuses on a specific subclass of adaptive control, namely, learning-based adaptive control. As systems evolve during time or are exposed to unstructured environments, it is expected that some of their characteristics may change. This book offers a new perspective about how to deal with these variations. By merging together Model-Free and Model-Based learning algorithms, the author demonstrates, using a number of mechatronic examples, how the learning process can be shortened and optimal control performance can be reached and maintained.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e1. Some Mathematical Tools 2. Adaptive Control: An Overview 3. Extremum Seeking-Based Iterative Feedback Gains Tuning Theory 4. Extremum Seeking-Based Indirect Adaptive Control 5. Extremum Seeking-Based Real-Time Parametric Identification for Nonlinear Systems 6. Extremum Seeking-Based Iterative Learning Model Predictive Control (ESILC-MPC)\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Mechanical engineering [\u003ca title=\"See our other books on Mechanical engineering\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mechanical%20engineering%20%5BTGB%5D%22\"\u003eTGB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Freshly Printed Books","offers":[{"title":"Default Title","offer_id":46649599557912,"sku":"9780128031360","price":85.69,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780128031360_25521d15-369a-48af-86c8-b14dbc14aa33.jpg?v=1694353131"},{"product_id":"data-simplification-taming-information-with-open-source-tools-paperback-9780128037812","title":"Data Simplification; Taming Information With Open Source Tools (Paperback) 9780128037812","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eData Simplification\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eTaming Information With Open Source Tools\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cem\u003e\u003cp\u003eThis comprehensive book teaches readers how to collect, categorize, simplify, and make sense of data using a step -by-step methodology that includes data simplication methods, open source tools, free utilities and snippets of code that can be reused and repurposed to simplify data.\u003c\/p\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eJules J. Berman (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780128037812\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback, published 9 March 2016\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e398 pages\u003cbr\u003e23.4 x 19 x 2.5 cm, 0.84 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\"As there is a \"gold rush\" encouraging the workforce training of data scientists, this gritty \"Rules of the Road\" monograph should serve as a constant companion for modern data scientists. Berman convincingly portrays the value of programmers and analysts who have facility with Perl, Python, or Ruby and who understand the critical role of metadata, indexing, and data visualization. These professionals will be high on my shopping list of talent to add to our biomedical informatics team in Pittsburgh.\"\u003c\/p\u003e \u003cp\u003e\"\u003ci\u003eData Simplification\u003c\/i\u003e provides easy, free solutions to the unintended consequences of data complexity. This book should be the first (and probably most important) guide to success in the data sciences. I will be providing copies to my trainees, programmers, analysts, and faculty, as required reading.\" \u003cb\u003e--Michael J. Becich, MD, PhD, Associate Vice-Chancellor for Informatics in the Health Sciences, Chairman and Distinguished University Professor, Department of Biomedical Informatics, Director, Center for Commercial Application (CCA) of Healthcare Data, \u003c\/b\u003e\u003cb\u003eUniversity of Pittsburgh School of Medicine\u003c\/b\u003e\u003c\/p\u003e\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003ci\u003eData Simplification: Taming Information With Open Source Tools \u003c\/i\u003eaddresses\u003ci\u003e \u003c\/i\u003ethe simple fact that modern data is too big and complex to analyze in its native form. Data simplification is the process whereby large and complex data is rendered usable. Complex data must be simplified before it can be analyzed, but the process of data simplification is anything but simple, requiring a specialized set of skills and tools. \u003c\/p\u003e  \u003cp\u003eThis book provides data scientists from every scientific discipline with the methods and tools to simplify their data for immediate analysis or long-term storage in a form that can be readily repurposed or integrated with other data.\u003c\/p\u003e  \u003cp\u003eDrawing upon years of practical experience, and using numerous examples and use cases, Jules Berman discusses the principles, methods, and tools that must be studied and mastered to achieve data simplification, open source tools, free utilities and snippets of code that can be reused and repurposed to simplify data, natural language processing and machine translation as a tool to simplify data, and data summarization and visualization and the role they play in making data useful for the end user.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e1. The Simple Life2. Structuring Text3. Indexing Text4. Understanding Your Data5. Identifying and Deidentifying Data6. Giving Meaning to Data7. Object-oriented data8. Problem simplification \u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Databases [\u003ca title=\"See our other books on Databases\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Databases%20%5BUN%5D%22\"\u003eUN\u003c\/a\u003e], Information technology: general issues [\u003ca title=\"See our other books on Information technology: general issues\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Information%20technology:%20general%20issues%20%5BUB%5D%22\"\u003eUB\u003c\/a\u003e], Library, archive \u0026amp; information management [\u003ca title=\"See our other books on Library, archive \u0026amp; information management\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Library,%20archive%20\u0026amp;%20information%20management%20%5BGLC%5D%22\"\u003eGLC\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Freshly Printed Books","offers":[{"title":"Default Title","offer_id":46649619022104,"sku":"9780128037812","price":35.76,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780128037812.jpg?v=1694102188"},{"product_id":"evolution-of-knowledge-science-myth-to-medicine-intelligent-internet-based-humanist-machines-paperback-9780128054789","title":"Evolution of Knowledge Science; Myth to Medicine: Intelligent Internet-Based Humanist Machines (Paperback) 9780128054789","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eEvolution of Knowledge Science\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eMyth to Medicine: Intelligent Internet-Based Humanist Machines\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cem\u003e\u003cp\u003eA thorough examination of how to design and build the next generation of intelligent machines to solve social and environmental problems\u003c\/p\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eSyed V. Ahamed (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780128054789\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback, published 11 November 2016\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e578 pages\u003cbr\u003e23.4 x 19 x 3.6 cm, 1.18 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\"Information Science is on the cusp of defining the transition from Big Data to Knowledge. This movement is being fueled by an urgency in addressing grand challenges in fields as diverse as health, public safety and climate change. Domain experts in these fields are looking to information science to provide a quantitative basis for solving hard problems in their data-intensive fields....Prof. Ahamed’s book represents a rigorous and optimistic declaration of this revolutionary trend. I recommend it heartily to teachers and students in communications and computing, and to those in pursuit of incisive mathematical philosophy.\" \u003cb\u003e--From the Foreword by Professor Dr. Nikil Jayant, Eminent Scholar (Emeritus), Georgia Research Alliance\u003c\/b\u003e\u003c\/p\u003e\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003ci\u003eEvolution of Knowledge Science: Myth to Medicine: Intelligent Internet-Based Humanist Machines\u003c\/i\u003e explains how to design and build the next generation of intelligent machines that solve social and environmental problems in a systematic, coherent, and optimal fashion. The book brings together principles from computer and communication sciences, electrical engineering, mathematics, physics, social sciences, and more to describe computer systems that deal with knowledge, its representation, and how to deal with knowledge centric objects. Readers will learn new tools and techniques to measure, enhance, and optimize artificial intelligence strategies for efficiently searching through vast knowledge bases, as well as how to ensure the security of information in open, easily accessible, and fast digital networks. \u003c\/p\u003e  \u003cp\u003eAuthor Syed Ahamed joins the basic concepts from various disciplines to describe a robust and coherent knowledge sciences discipline that provides readers with tools, units, and measures to evaluate the flow of knowledge during course work or their research. He offers a unique academic and industrial perspective of the concurrent dynamic changes in computer and communication industries based upon his research. The author has experience both in industry and in teaching graduate level telecommunications and network architecture courses, particularly those dealing with applications of networks in education.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePart I: Knowledge, Wisdom and Values\u003c\/p\u003e \u003cp\u003eSection I: From Early Thinker to Social Scientists\u003c\/p\u003e \u003cp\u003eChapter 1. Knowledge and Wisdom Across Cultures\u003c\/p\u003e \u003cp\u003eChapter 2. From Philosophers to Knowledge Machines\u003c\/p\u003e \u003cp\u003eChapter 3. Affirmative Knowledge and Positive Human Nature\u003c\/p\u003e \u003cp\u003eChapter 4. Negative Knowledge and Aggressive Human Nature\u003c\/p\u003e \u003cp\u003eChapter 5. Role of Devices, Computers and Networks\u003c\/p\u003e \u003cp\u003eSection II: Information Machines and Social Progress\u003c\/p\u003e \u003cp\u003eChapter 6. Recent Changes to the Structure of Knowledge\u003c\/p\u003e \u003cp\u003eChapter 7. Origin and Structure of Knowledge Energy\u003c\/p\u003e \u003cp\u003eChapter 8. Bands of Knowledge\u003c\/p\u003e \u003cp\u003eChapter 9. Frustums of Artificial Behavior\u003c\/p\u003e \u003cp\u003eChapter 10. Computer-Aided Knowledge Design and Validation\u003c\/p\u003e \u003cp\u003eSection III: Knowledge Science and Social Influence\u003c\/p\u003e \u003cp\u003eChapter 11. Knowledge and Information Ethics\u003c\/p\u003e \u003cp\u003eChapter 12. From Primal Thinking to Potential Computing\u003c\/p\u003e \u003cp\u003eChapter 13. Action (VF) ? (*) ? Object (NO) Based Processors and Machines\u003c\/p\u003e \u003cp\u003eChapter 14. Aphorism and Truism in Knowledge Domain\u003c\/p\u003e \u003cp\u003eChapter 15. Timing Sequences and Influence of Time\u003c\/p\u003e \u003cp\u003ePart II: Summary\u003c\/p\u003e \u003cp\u003eSection I. The Scientific basis for Knowledge Flow\u003c\/p\u003e \u003cp\u003eChapter 16. General Flow Theory of Knowledge\u003c\/p\u003e \u003cp\u003eChapter 17. Transmission Flow Theory of Knowledge\u003c\/p\u003e \u003cp\u003eChapter 18. Quantum Flow Theory of Knowledge\u003c\/p\u003e \u003cp\u003eChapter 19. Inspiration Flow Theory of Knowledge\u003c\/p\u003e \u003cp\u003eChapter 20. Dynamic Nature of Knowledge: Fragmentation and Flow\u003c\/p\u003e \u003cp\u003eSection II: Preface\u003c\/p\u003e \u003cp\u003eChapter 21. Knowledge Potential and Utility\u003c\/p\u003e \u003cp\u003eChapter 22. Elements of Knowledge as Elements in Nature\u003c\/p\u003e \u003cp\u003eChapter 23. Knowledge Element Machine Design: Pathways of Knowledge in Machines\u003c\/p\u003e \u003cp\u003eChapter 24. Elements of Knowledge in Societies\u003c\/p\u003e \u003cp\u003eChapter 25. Role of Human Discretion in Society and Its Impact on Ecosystems\u003c\/p\u003e \u003cp\u003eSection III: Preface\u003c\/p\u003e \u003cp\u003eChapter 26. Scientific Foundations of Knowledge\u003c\/p\u003e \u003cp\u003eChapter 27. Real Space, Knowledge Space and Computational Space\u003c\/p\u003e \u003cp\u003eChapter 28. General Structure of Knowledge (no*? vf and vf*? no)\u003c\/p\u003e \u003cp\u003eChapter 29. The Architecture of a Mind-Machine\u003c\/p\u003e \u003cp\u003eChapter 30. The Architecture of a Medical Machine\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Expert systems \/ knowledge-based systems [\u003ca title=\"See our other books on Expert systems \/ knowledge-based systems\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Expert%20systems%20\/%20knowledge-based%20systems%20%5BUYQE%5D%22\"\u003eUYQE\u003c\/a\u003e], Artificial intelligence [\u003ca title=\"See our other books on Artificial intelligence\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Artificial%20intelligence%20%5BUYQ%5D%22\"\u003eUYQ\u003c\/a\u003e], Computer programming \/ software development [\u003ca title=\"See our other books on Computer programming \/ software development\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20programming%20\/%20software%20development%20%5BUM%5D%22\"\u003eUM\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Freshly Printed Books","offers":[{"title":"Default Title","offer_id":46649741148440,"sku":"9780128054789","price":53.35,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780128054789.jpg?v=1694103245"},{"product_id":"deep-learning-for-medical-image-analysis-paperback-9780128104088","title":"Deep Learning for Medical Image Analysis (Paperback \/ softback) 9780128104088","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eDeep Learning for Medical Image Analysis\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003eApplies deep learning methods to medical imaging, providing a clear understanding of the principles and methods of neural network and deep learning\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eS. Kevin Zhou (Edited by), Hayit Greenspan (Edited by), Dinggang Shen (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780128104088\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback \/ softback, published 31 January 2017\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e458 pages\u003cbr\u003e23.4 x 19 x 2.9 cm, 0.97 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eDeep learning is providing exciting solutions for medical image analysis problems and is seen as a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component have been applied to medical image detection, segmentation and registration, and computer-aided analysis, using a wide variety of application areas. \u003ci\u003eDeep Learning for Medical Image Analysis\u003c\/i\u003e is a great learning resource for academic and industry researchers in medical imaging analysis, and for graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003ePART 1: INTRODUCTION \u003c\/b\u003e1. An introduction to neural network and deep learning (covering CNN, RNN, RBM, Autoencoders) \u003ci\u003eHeung-Il Suk\u003c\/i\u003e 2. An Introduction to Deep Convolutional Neural Nets for Computer Vision \u003ci\u003eSuraj Srinivas, Ravi K. Sarvadevabhatla, Konda R. Mopuri, Nikita Prabhu, Srinivas S.S. Kruthiventi and R. Venkatesh Babu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART 2: MEDICAL IMAGE DETECTION AND RECOGNITION \u003c\/b\u003e3. Efficient Medical Image Parsing \u003ci\u003eFlorin C. Ghesu, Bogdan Georgescu and Joachim Hornegger \u003c\/i\u003e4. Multi-Instance Multi-Stage Deep Learning for Medical Image Recognition \u003ci\u003eZhennan Yan, Yiqiang Zhan, Shaoting Zhang, Dimitris Metaxas and Xiang Sean Zhou \u003c\/i\u003e5. Automatic Interpretation of Carotid Intima–Media Thickness Videos Using Convolutional Neural Networks  \u003ci\u003eNima Tajbakhsh, Jae Y. Shin, R. Todd Hurst, Christopher B. Kendall and Jianming Liang \u003c\/i\u003e6. Deep Cascaded Networks for Sparsely Distributed Object Detection from Medical Images \u003ci\u003eHao Chen, Qi Dou, Lequan Yu, Jing Qin, Lei Zhao, Vincent C.T. Mok, Defeng Wang, Lin Shi and Pheng-Ann Heng \u003c\/i\u003e7. Deep Voting and Structured Regression for Microscopy Image Analysis \u003ci\u003eYuanpu Xie, Fuyong Xing and Lin Yang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART 3 MEDICAL IMAGE SEGMENTATION \u003c\/b\u003e8. Deep Learning Tissue Segmentation in Cardiac Histopathology Images \u003ci\u003eJeffrey J. Nirschl, Andrew Janowczyk, Eliot G. Peyster, Renee Frank, Kenneth B. Margulies, Michael D. Feldman and Anant Madabhushi \u003c\/i\u003e9. Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching \u003ci\u003eYanrong Guo, Yaozong Gao and Dinggang Shen \u003c\/i\u003e10. Characterization of Errors in Deep Learning-Based Brain MRI Segmentation \u003ci\u003eAkshay Pai, Yuan-Ching Teng, Joseph Blair, Michiel Kallenberg, Erik B. Dam, Stefan Sommer, Christian Igel and Mads Nielsen\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART 4 MEDICAL IMAGE REGISTRATION \u003c\/b\u003e11. Scalable High Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning \u003ci\u003eShaoyu Wang, Minjeong Kim, Guorong Wu and Dinggang Shen \u003c\/i\u003e12. Convolutional Neural Networks for Robust and Real-Time 2-D\/3-D Registration \u003ci\u003eShun Miao, Jane Z. Wang and Rui Liao\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART 5 COMPUTER-AIDED DIAGNOSIS AND DISEASE QUANTIFICATION \u003c\/b\u003e13. Chest Radiograph Pathology Categorization via Transfer Learning \u003ci\u003eIdit Diamant, Yaniv Bar, Ofer Geva, Lior Wolf, Gali Zimmerman, Sivan Lieberman, Eli Konen and Hayit Greenspan \u003c\/i\u003e14. Deep Learning Models for Classifying Mammogram Exams Containing Unregistered Multi-View Images and Segmentation Maps of Lesions \u003ci\u003eGustavo Carneiro, Jacinto Nascimento and Andrew P. Bradley \u003c\/i\u003e15. Randomized Deep Learning Methods for Clinical Trial Enrichment and Design in Alzheimer’s Disease \u003ci\u003eVamsi K. Ithapu, Vikas Singh and Sterling C. Johnson \u003c\/i\u003e16. Deep Networks and Mutual Information Maximization for Cross-Modal Medical Image Synthesis \u003ci\u003eRaviteja Vemulapalli, Hien Van Nguyen and S.K. Zhou \u003c\/i\u003e17. Natural Language Processing for Large-Scale Medical Image Analysis Using Deep Learning \u003ci\u003eHoo-Chang Shin, Le Lu and Ronald M. Summers\u003c\/i\u003e\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Image processing [\u003ca title=\"See our other books on Image processing\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Image%20processing%20%5BUYT%5D%22\"\u003eUYT\u003c\/a\u003e], Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Enterprise software [\u003ca title=\"See our other books on Enterprise software\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Enterprise%20software%20%5BUFL%5D%22\"\u003eUFL\u003c\/a\u003e], Medical bioinformatics [\u003ca title=\"See our other books on Medical bioinformatics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Medical%20bioinformatics%20%5BMBF%5D%22\"\u003eMBF\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Freshly Printed Books","offers":[{"title":"Default Title","offer_id":46649759170840,"sku":"9780128104088","price":74.79,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780128104088_5d406b9e-e747-47e6-b1a6-4b672ae05dff.jpg?v=1694353243"},{"product_id":"temporal-data-mining-via-unsupervised-ensemble-learning-paperback-9780128116548","title":"Temporal Data Mining via Unsupervised Ensemble Learning (Paperback) 9780128116548","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eTemporal Data Mining via Unsupervised Ensemble Learning\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cp\u003ePresents an overview of temporal data mining, knowledge of temporal data clustering, and ensemble learning techniques, including theory and practice\u003c\/p\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eYun Yang (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780128116548\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback, published 18 November 2016\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e172 pages\u003cbr\u003e23.4 x 19 x 1.2 cm, 0.45 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eApprox.158 pages\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e1. Introduction2. Temporal Data Mining3. Temporal Data Clustering4. Ensemble Learning5. HMM-Based Hybrid Meta-Clustering in Association With Ensemble Technique6. Unsupervised Learning via an Iteratively Constructed Clustering Ensemble7. Temporal Data Clustering via a Weighted Clustering Ensemble With Different Representations8. Conclusions, Future Work\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Expert systems \/ knowledge-based systems [\u003ca title=\"See our other books on Expert systems \/ knowledge-based systems\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Expert%20systems%20\/%20knowledge-based%20systems%20%5BUYQE%5D%22\"\u003eUYQE\u003c\/a\u003e], Databases [\u003ca title=\"See our other books on Databases\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Databases%20%5BUN%5D%22\"\u003eUN\u003c\/a\u003e], Library, archive \u0026amp; information management [\u003ca title=\"See our other books on Library, archive \u0026amp; information management\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Library,%20archive%20\u0026amp;%20information%20management%20%5BGLC%5D%22\"\u003eGLC\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Freshly Printed Books","offers":[{"title":"Default Title","offer_id":46649789677848,"sku":"9780128116548","price":41.39,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780128116548.jpg?v=1694103424"},{"product_id":"meta-analytics-consensus-approaches-and-system-patterns-for-data-analysis-paperback-9780128146231","title":"Meta-Analytics; Consensus Approaches and System Patterns for Data Analysis (Paperback) 9780128146231","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eMeta-Analytics\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eConsensus Approaches and System Patterns for Data Analysis\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cem\u003e\u003cp\u003ePresents system patterns and other means for practitioners of data analytics to build better systems\u003c\/p\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eSteven Simske (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780128146231, Elsevier Science\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback, published 13 March 2019\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e340 pages\u003cbr\u003e23.4 x 19 x 2.2 cm, 0.7 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003ci\u003eMeta-Analytics: Consensus Approaches and System Patterns for Data Analysis\u003c\/i\u003e presents an exhaustive set of patterns for data science to use on any machine learning based data analysis task. The book virtually ensures that at least one pattern will lead to better overall system behavior than the use of traditional analytics approaches. The book is ‘meta’ to analytics, covering general analytics in sufficient detail for readers to engage with, and understand, hybrid or meta- approaches. The book has relevance to machine translation, robotics, biological and social sciences, medical and healthcare informatics, economics, business and finance. \u003c\/p\u003e  \u003cp\u003eInn addition, the analytics within can be applied to predictive algorithms for everyone from police departments to sports analysts.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e1. Ground truthing2. Experiment design3. Meta-Analytic design patterns4. Sensitivity analysis and big system engineering5. Multi-path predictive selection6. Modeling and model fitting: including Antibody model, stem-differentiated cell model, and chemical, physical and environmental models for greater diversity in form7. Synonym-antonym and Reinforce-Void patterns and their value in data consensus, data anonymization, and data normalization8. Meta-analytics as analytics around analytics (functional metrics, entropy, EM). Ingesting statistical approaches for specific domains and generalizing them for data hybrid systems9. System design optimization (entropy, error variance, coupling minimization F-score)10. Aleatory techniques\/expert system techniques…tie to ground truthing and error testing11. Applications: machine translation, robotics, biological and social sciences, medical and healthcare informatics, economics, business and finance12. Discussion and Conclusions, and the Future of Data\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Information technology: general issues [\u003ca title=\"See our other books on Information technology: general issues\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Information%20technology:%20general%20issues%20%5BUB%5D%22\"\u003eUB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Morgan Kaufmann","offers":[{"title":"Default Title","offer_id":46649940246808,"sku":"9780128146231","price":50.47,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780128146231.jpg?v=1694104654"},{"product_id":"advanced-machine-vision-paradigms-for-medical-image-analysis-paperback-9780128192955","title":"Advanced Machine Vision Paradigms for Medical Image Analysis (Paperback) 9780128192955","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eAdvanced Machine Vision Paradigms for Medical Image Analysis\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cp\u003eAdvanced review of soft computing techniques and their applications in medical image analysis\u003c\/p\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eTapan K. Gandhi (Edited by), Siddhartha Bhattacharyya (Edited by), Sourav De (Edited by), Debanjan Konar (Edited by), Sandip Dey (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780128192955\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback, published 13 August 2020\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e308 pages\u003cbr\u003e22.9 x 15.1 x 2 cm, 0.5 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eApprox.290 pages\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e1. Computer Aided Decision Support System for symmetry based prenatal congenital heart defects 2. Morphological Extreme Learning Machines applied to the detection and classification of mammary lesions 3. 4D Medical Image Analysis: A Systematic Study on Applications, Challenges and Future Research Directions 4. Comparative Analysis of Hybrid Fusion Algorithms using Neurocysticercosis, Neoplastic, Alzheimer's and Astrocytoma Disease affected Multimodality Medical Images 5. Binary Descriptors Design for the Automatic Detection of Coronary Arteries using Metaheuristics 6. A Cognitive Perception on Content Based Image Retrieval using Advanced Soft Computing Paradigm 7. Early detection of Parkinson’s Disease Using Data Mining Techniques from Multi-Modal Clinical Data 8. Contrast Improvement of Medical Images Using Advanced Fuzzy Logic Based Technique 9. Intelligent Heart Disease Prediction On Physical and Mental Parameters: A ML Based IoT and Big Data Application \u0026amp; Analysis\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Artificial intelligence [\u003ca title=\"See our other books on Artificial intelligence\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Artificial%20intelligence%20%5BUYQ%5D%22\"\u003eUYQ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Freshly Printed Books","offers":[{"title":"Default Title","offer_id":46650145112344,"sku":"9780128192955","price":102.79,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780128192955.jpg?v=1694106027"},{"product_id":"practical-machine-learning-for-data-analysis-using-python-paperback-9780128213797","title":"Practical Machine Learning for Data Analysis Using Python (Paperback) 9780128213797","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003ePractical Machine Learning for Data Analysis Using Python\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003eOffers a practical Python-based toolkit for data analysis using different machine learning techniques\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eAbdulhamit Subasi (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780128213797\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback, published 7 June 2020\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e534 pages\u003cbr\u003e23.4 x 19 x 3.3 cm, 1.11 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eApprox.520 pages\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e1. Introduction 2. Data preprocessing3. Machine learning techniques4. Classification examples for healthcare5. Other classification examples6. Regression examples7. Clustering examples\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Freshly Printed Books","offers":[{"title":"Default Title","offer_id":46650264682776,"sku":"9780128213797","price":87.38,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780128213797.jpg?v=1694107158"},{"product_id":"machine-learning-big-data-and-iot-for-medical-informatics-paperback-9780128217771","title":"Machine Learning, Big Data, and IoT for Medical Informatics (Paperback) 9780128217771","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eMachine Learning, Big Data, and IoT for Medical Informatics\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003e\u003ci\u003eProvides a thorough accounting of semantic information and structure of data with simple and straightforward examples.\u003c\/i\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003ePardeep Kumar (Edited by), Yugal Kumar (Edited by), Mohamed A. Tawhid (Edited by), Fatos Xhafa (Series edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780128217771\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback, published 16 June 2021\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e458 pages, Approx. 110 illustrations\u003cbr\u003e23.5 x 19 x 2.9 cm, 0.93 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eApprox.432 pages\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e1. Predictive analytics and machine learning for medical informatics: A survey of tasks and techniques 2. Geolocation-aware IoT and cloud-fog-based solutions for healthcare 3. Machine learning vulnerability in medical imaging 4. Skull stripping and tumor detection using 3D U-Net\u003cb\u003e \u003c\/b\u003e5. Cross color dominant deep autoencoder for quality enhancement of laparoscopic video: A hybrid deep learning and range-domain filtering-based approach 6. Estimating the respiratory rate from ECG and PPG using machine learning techniques 7. Machine learning-enabled Internet of Things for medical informatics 8. Edge detection-based segmentation for detecting skin lesions 9.\u003cb\u003e \u003c\/b\u003eA review of deep learning approaches in glove-based gesture classification 10. An ensemble approach for evaluating the cognitive performance of human population at high altitude 11. Machine learning in expert systems for disease diagnostics in human healthcare 12. An entropy-based hybrid feature selection approach for medical datasets 13. Machine learning for optimizing healthcare resources\u003cb\u003e \u003c\/b\u003e14. Interpretable semi-supervised classifier for predicting cancer stages 15. Applications of blockchain technology in smart healthcare: An overview 16. Prediction of leukemia by classification and clustering techniques 17. Performance evaluation of fractal features toward seizure detection from electroencephalogram signals 18. Integer period discrete Fourier transform-based algorithm for the identification of tandem repeats in the DNA sequences 19. A blockchain solution for the privacy of patients' medical data 20. A novel approach for securing e-health application in a cloud environment 21. An ensemble classifier approach for thyroid disease diagnosis using the AdaBoostM algorithm 22. A review of deep learning models for medical diagnosis 23. Machine learning in precision medicine\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Expert systems \/ knowledge-based systems [\u003ca title=\"See our other books on Expert systems \/ knowledge-based systems\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Expert%20systems%20\/%20knowledge-based%20systems%20%5BUYQE%5D%22\"\u003eUYQE\u003c\/a\u003e], Artificial intelligence [\u003ca title=\"See our other books on Artificial intelligence\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Artificial%20intelligence%20%5BUYQ%5D%22\"\u003eUYQ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Freshly Printed Books","offers":[{"title":"Default Title","offer_id":46650273956120,"sku":"9780128217771","price":90.69,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780128217771.jpg?v=1694107206"},{"product_id":"optimum-path-forest-theory-algorithms-and-applications-paperback-9780128226889","title":"Optimum-Path Forest; Theory, Algorithms, and Applications (Paperback) 9780128226889","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eOptimum-Path Forest\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eTheory, Algorithms, and Applications\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cem\u003eHelps readers gain an understanding of the methods, underlying theory and applications of Optimum-Path Forest (OPF)\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eAlexandre Xavier Falcao (Edited by), João Paulo Papa (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780128226889, Elsevier Science\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback, published 24 January 2022\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e244 pages\u003cbr\u003e22.9 x 15.2 x 1.6 cm, 0.41 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eThe Optimum-Path Forest (OPF) classifier was first published in 2008 in its supervised and unsupervised versions with applications in medicine and image classification. Since then, it has expanded to a variety of other applications such as remote sensing, electrical and petroleum engineering, and biology. In recent years, multi-label and semi-supervised versions were also developed to handle video classification problems. The book presents the principles, algorithms and applications of Optimum-Path Forest, giving the theory and state-of-the-art as well as insights into future directions.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e1. Introduction 2. Theoretical Background and Related Works 3. Real-time application of OPF-based classifier in Snort IDS 4. Optimum-Path Forest and Active Learning Approaches for Content-Based Medical Image Retrieval 5. Hybrid and Modified OPFs for Intrusion Detection Systems and Large-Scale Problems 6. Detecting Atherosclerotic Plaque Calcifications of the Carotid Artery Through Optimum-Path Forest 7. Learning to Weight Similarity Measures with Siamese Networks: A Case Study on Optimum-Path Forest 8. An Iterative Optimum-Path Forest Framework for Clustering 9. Future Trends in Optimum-Path Forest Classification\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Academic Press","offers":[{"title":"Default Title","offer_id":46650292863256,"sku":"9780128226889","price":103.67,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780128226889.jpg?v=1694107307"},{"product_id":"adversarial-robustness-for-machine-learning-paperback-9780128240205","title":"Adversarial Robustness for Machine Learning (Paperback) 9780128240205","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eAdversarial Robustness for Machine Learning\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003eA complete overview of the field of adversarial robustness for machine learning models\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003ePin-Yu Chen (Author), Cho-Jui Hsieh (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780128240205\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback, published 25 August 2022\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e298 pages, Approx. 100 illustrations\u003cbr\u003e28.6 x 21.6 x 2 cm, 0.45 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eApprox.284 pages\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e1. White-box attack\u003cbr\u003e2. Soft-label Black-box Attack\u003cbr\u003e3. Decision-based attack\u003cbr\u003e4. Attack Transferibility\u003cbr\u003e5. Attacks in the physical world\u003cbr\u003e6. Convex relaxation Framework\u003cbr\u003e7. Layer-wise relaxation (primal algorithms)\u003cbr\u003e8. Dual approach\u003cbr\u003e9. Probabilistic veri?cation\u003cbr\u003e10. Adversarial training\u003cbr\u003e11. Certi?ed defense\u003cbr\u003e12. Randomization\u003cbr\u003e13. Detection methods\u003cbr\u003e14. Robustness of other machine learning models beyond neural networks\u003cbr\u003e15. NLP models\u003cbr\u003e16. Graph neural network\u003cbr\u003e17. Recommender systems\u003cbr\u003e18. Reinforcement Learning\u003cbr\u003e19. Speech models\u003cbr\u003e20. Multi-modal models\u003cbr\u003e21. Backdoor attack and defense\u003cbr\u003e22. Data poisoning attack and defense\u003cbr\u003e23. Transfer learning\u003cbr\u003e24. Explainability and interpretability\u003cbr\u003e25. 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Privacy and watermarking\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Artificial intelligence [\u003ca title=\"See our other books on Artificial intelligence\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Artificial%20intelligence%20%5BUYQ%5D%22\"\u003eUYQ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Freshly Printed Books","offers":[{"title":"Default Title","offer_id":46650400276760,"sku":"9780128240205","price":75.49,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780128240205.jpg?v=1694108309"},{"product_id":"cognitive-data-models-for-sustainable-environment-paperback-9780128240380","title":"Cognitive Data Models for Sustainable Environment (Paperback) 9780128240380","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eCognitive Data Models for Sustainable Environment\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cp\u003eIntroduces novel cognitive models and intelligent techniques needed to address environmental pollution for the well-being of the global environment\u003c\/p\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eSiddhartha Bhattacharyya (Edited by), Naba Kumar Mondal (Edited by), Koushik Mondal (Edited by), Jyoti Prakash Singh (Edited by), Kolla Bhanu Prakash (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780128240380, Elsevier Science\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback, published 22 September 2021\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e316 pages\u003cbr\u003e22.9 x 15.2 x 2.1 cm, 0.45 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003ci\u003eCognitive Models for Sustainable Environment\u003c\/i\u003e reviews the fundamental concepts of gathering, processing and analyzing data from batch processes, along with a review of intelligent and cognitive tools that can be used. The book is centered on evolving novel intelligent\/cognitive models and algorithms to develop sustainable solutions for the mitigation of environmental pollution. It unveils intelligent and cognitive models to address issues related to the effective monitoring of environmental pollution and sustainable environmental design. As such, the book focuses on the overall well-being of the global environment for better sustenance and livelihood.\u003c\/p\u003e  \u003cp\u003eThe book covers novel cognitive models for effective environmental pollution data management at par with the standards laid down by the World Health Organization. Every chapter is supported by real-life case studies, illustrative examples and video demonstrations that enlighten readers.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e1. Multidimensional controlling properties of biofabricated silver-nanoparticles on different mosquito species 2. Machine learningeenabled cognitive approaches for handling IoT-based environmental data 3. Evolution of sustainable environment: a cognitive outlook 4. Application of nanotechnology in pesticides adsorption with statistical optimization and modeling 5. Sustainability issues in upcoming wastewater treatment plants at Patna 6. Community approach toward disaster resilience 7. ZnO nanoparticles: a facile synthesized agent for removing dye from aqueous solution in an ecofriendly way 8. Optimization of rural indoor kitchen structure and minimizing the pollution load: a sustainable environmental modeling approach 9. IoT-based health care data analytical paradigm using blockchain technology 10. Environmental pain with human beauty: emerging environmental hazards attributed to cosmetic ingredients and packaging 11. Indian rural housing: an approach toward sustainability\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Artificial intelligence [\u003ca title=\"See our other books on Artificial intelligence\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Artificial%20intelligence%20%5BUYQ%5D%22\"\u003eUYQ\u003c\/a\u003e], Other manufacturing technologies [\u003ca title=\"See our other books on Other manufacturing technologies\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Other%20manufacturing%20technologies%20%5BTDP%5D%22\"\u003eTDP\u003c\/a\u003e], Biotechnology [\u003ca title=\"See our other books on Biotechnology\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Biotechnology%20%5BTCB%5D%22\"\u003eTCB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Academic Press","offers":[{"title":"Default Title","offer_id":46650400571672,"sku":"9780128240380","price":94.58,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780128240380.jpg?v=1694108312"},{"product_id":"deep-network-design-for-medical-image-computing-principles-and-applications-paperback-9780128243831","title":"Deep Network Design for Medical Image Computing; Principles and Applications (Paperback) 9780128243831","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eDeep Network Design for Medical Image Computing\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003ePrinciples and Applications\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cem\u003eHelps readers deep learning design methods specifically developed to solve medical problems\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eHaofu Liao (Author), S. Kevin Zhou (Author), Jiebo Luo (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780128243831, Elsevier Science\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback, published 30 August 2022\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e264 pages, 75 illustrations (30 in full color)\u003cbr\u003e23.5 x 19 x 1.8 cm, 0.52 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003ci\u003eDeep Network Design for Medical Image Computing: Principles and Applications\u003c\/i\u003e covers a range of MIC tasks and discusses design principles of these tasks for deep learning approaches in medicine. These include skin disease classification, vertebrae identification and localization, cardiac ultrasound image segmentation, 2D\/3D medical image registration for intervention, metal artifact reduction, sparse-view artifact reduction, etc. For each topic, the book provides a deep learning-based solution that takes into account the medical or biological aspect of the problem and how the solution addresses a variety of important questions surrounding architecture, the design of deep learning techniques, when to introduce adversarial learning, and more. \u003c\/p\u003e  \u003cp\u003eThis book will help graduate students and researchers develop a better understanding of the deep learning design principles for MIC and to apply them to their medical problems.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e1. Introduction\u003cbr\u003e2. Deep Learning Basics\u003cbr\u003e3. Classification: Lesion and Disease Recognition\u003cbr\u003e4. Detection: Vertebrae Localization and Identification\u003cbr\u003e5. Segmentation: Intracardiac Echocardiography Contouring\u003cbr\u003e6. Registration: 2D\/3D Medical Image Registration\u003cbr\u003e7. Reconstruction: Supervised Artifact Reduction\u003cbr\u003e8. Reconstruction: Unsupervised Artifact Reduction\u003cbr\u003e9. Synthesis: Novel View Synthesis\u003cbr\u003e10. Challenges and Future Directions\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Computer vision [\u003ca title=\"See our other books on Computer vision\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20vision%20%5BUYQV%5D%22\"\u003eUYQV\u003c\/a\u003e], Neural networks \u0026amp; fuzzy systems [\u003ca title=\"See our other books on Neural networks \u0026amp; fuzzy systems\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Neural%20networks%20\u0026amp;%20fuzzy%20systems%20%5BUYQN%5D%22\"\u003eUYQN\u003c\/a\u003e], Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Artificial intelligence [\u003ca title=\"See our other books on Artificial intelligence\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Artificial%20intelligence%20%5BUYQ%5D%22\"\u003eUYQ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Academic Press","offers":[{"title":"Default Title","offer_id":46650405388568,"sku":"9780128243831","price":75.49,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780128243831.jpg?v=1694108352"},{"product_id":"cyber-physical-systems-ai-and-covid-19-paperback-9780128245576","title":"Cyber-Physical Systems; AI and COVID-19 (Paperback) 9780128245576","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eCyber-Physical Systems\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eAI and COVID-19\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cem\u003eExplores the development and application of science, engineering and technology for COVID-19\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eRamesh Chandra Poonia (Edited by), Basant Agarwal (Edited by), Sandeep Kumar (Edited by), Mohammad S. Khan (Edited by), Goncalo Marques (Edited by), Janmenjoy Nayak (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780128245576, Elsevier Science\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback, published 2 November 2021\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e278 pages\u003cbr\u003e22.9 x 15.2 x 1.8 cm, 0.43 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003ci\u003eCyber-Physical Systems: AI and COVID-19\u003c\/i\u003e highlights original research which addresses current data challenges in terms of the development of mathematical models, cyber-physical systems-based tools and techniques, and the design and development of algorithmic solutions, etc. It reviews the technical concepts of gathering, processing and analyzing data from cyber-physical systems (CPS) and reviews tools and techniques that can be used. This book will act as a resource to guide COVID researchers as they move forward with clinical and epidemiological studies on this outbreak, including the technical concepts of gathering, processing and analyzing data from cyber-physical systems (CPS).\u003c\/p\u003e  \u003cp\u003eThe major problem in the identification of COVID-19 is detection and diagnosis due to non-availability of medicine. In this situation, only one method, Reverse Transcription Polymerase Chain Reaction (RT-PCR) has been widely adopted and used for diagnosis. With the evolution of COVID-19, the global research community has implemented many machine learning and deep learning-based approaches with incremental datasets. However, finding more accurate identification and prediction methods are crucial at this juncture. \u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e1. AI-based implementation of decisive technology for prevention and fight with COVID-19  Alok Negi and Krishan Kumar\u003c\/p\u003e \u003cp\u003e2. Internet of Things-based smart helmet to detect possible COVID-19 infections  Chanchal Ahlawat and Rajalakshmi Krishnamurthi\u003c\/p\u003e \u003cp\u003e3. Role of mobile health in the situation of COVID-19 pandemics: pros and cons  Priyanka Payal and Rao Sunita\u003c\/p\u003e \u003cp\u003e4. Combating COVID-19 using object detection techniques for next-generation autonomous systems  Hrishikesh Shenai, Jay Gala, Kaustubh Kekre, Pranjal Chitale and Ruhina Karani\u003c\/p\u003e \u003cp\u003e5. Non-contact measurement system for COVID-19 vital signs to aid mass screening—An alternate approach  Vijay Jeyakumar, K. Nirmala and Sachin G. Sarate\u003c\/p\u003e \u003cp\u003e6. Evolving uncertainty in healthcare service interactions during COVID-19: Artificial Intelligence - a threat or support to value cocreation?  Sumit Saxena and Amritesh\u003c\/p\u003e \u003cp\u003e7. The COVID-19 outbreak: social media sentiment analysis of public reactions with a multidimensional perspective  Basant Agarwal, Vaishnavi Sharma, Priyanka Harjule, Vinita Tiwari and Ashish Sharma\u003c\/p\u003e \u003cp\u003e8. A new approach to predict COVID-19 using artificial neural networks  Soham Guhathakurata, Sayak Saha, Souvik Kundu, Arpita Chakraborty and Jyoti Sekhar Banerjee\u003c\/p\u003e \u003cp\u003e9. Rapid medical guideline systems for COVID-19 using database-centric modeling and validation of cyber-physical systems  Mani Padmanabhan\u003c\/p\u003e \u003cp\u003e10. Machine learning and security in Cyber Physical Systems  Neha V. Sharma, Narendra Singh Yadav and Saurabh Sharma\u003c\/p\u003e \u003cp\u003e11. Impact analysis of COVID-19 news headlines on global economy  Ananya Malik, Yash Tejas Javeri, Manav Shah and Ramchandra Mangrulkar\u003c\/p\u003e \u003cp\u003e12. Impact of COVID-19: a particular focus on Indian education system  Pushpa Gothwal, Bosky Dharmendra Sharma, Nandita Chaube and Nadeem Luqman\u003c\/p\u003e \u003cp\u003e13. Designing of Latent Dirichlet Allocation based prediction model to detect Midlife Crisis of losing jobs due to prolonged lockdown for COVID-19  B. Das, B. Das, A. Chatterjee and A. Das\u003c\/p\u003e \u003cp\u003e14. Autonomous robotic system for ultraviolet disinfection  Riki Patel, Harshal Sanghvi and Abhijit S. Pandya\u003c\/p\u003e \u003cp\u003e15. Emerging health start-ups for economic feasibility: opportunities during COVID-19  Shweta Nanda\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e], Artificial intelligence [\u003ca title=\"See our other books on Artificial intelligence\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Artificial%20intelligence%20%5BUYQ%5D%22\"\u003eUYQ\u003c\/a\u003e], Robotics [\u003ca title=\"See our other books on Robotics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Robotics%20%5BTJFM1%5D%22\"\u003eTJFM1\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Academic Press","offers":[{"title":"Default Title","offer_id":46650409943320,"sku":"9780128245576","price":96.19,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780128245576.jpg?v=1694108378"}],"url":"https:\/\/freshlyprintedbooks.co.uk\/collections\/machine-learning.oembed?page=5","provider":"Freshly Printed Books","version":"1.0","type":"link"}