{"product_id":"machine-learning-a-concise-introduction-hardback-9781394325252","title":"Machine Learning; A Concise Introduction (Hardback) 9781394325252","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eMachine Learning\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eA Concise Introduction\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eSteven W. Knox (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394325252, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 28 January 2026\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e432 pages\u003cbr\u003e25.6 x 18 x 2.5 cm, 0.975 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\u003eNew edition of a PROSE award finalist title on core concepts for machine learning, updated with the latest developments in the field, now with Python and R source code side-by-side\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003e\u003ci\u003eMachine Learning\u003c\/i\u003e is a comprehensive text on the core concepts, approaches, and applications of machine learning. It presents fundamental ideas, terminology, and techniques for solving applied problems in classification, regression, clustering, density estimation, and dimension reduction. New content for this edition includes chapter expansions which provide further computational and algorithmic insights to improve reader understanding. This edition also revises several chapters to account for developments since the prior edition. \u003c\/p\u003e\n\u003cp\u003eIn this book, the design principles behind the techniques are emphasized, including the bias-variance trade-off and its influence on the design of ensemble methods, enabling readers to solve applied problems more efficiently and effectively. This book also includes methods for optimization, risk estimation, model selection, and dealing with biased data samples and software limitations — essential elements of most applied projects. \u003c\/p\u003e\n\u003cp\u003eWritten by an expert in the field, this important resource: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eIllustrates many classification methods with a single, running example, highlighting similarities and differences between methods\u003c\/li\u003e\n\u003cli\u003ePresents side-by-side Python and R source code which shows how to apply and interpret many of the techniques covered\u003c\/li\u003e\n\u003cli\u003eIncludes many thoughtful exercises as an integral part of the text, with an appendix of selected solutions\u003c\/li\u003e\n\u003cli\u003eContains useful information for effectively communicating with clients on both technical and ethical topics\u003c\/li\u003e\n\u003cli\u003eDetails classification techniques including likelihood methods, prototype methods, neural networks, classification trees, and support vector machines\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eA volume in the popular Wiley Series in Probability and Statistics, \u003ci\u003eMachine Learning\u003c\/i\u003e offers the practical information needed for an understanding of the methods and application of machine learning for advanced undergraduate and beginner graduate students, data science and machine learning practitioners, and other technical professionals in adjacent fields.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xi\u003c\/p\u003e \u003cp\u003eOrganization — How to Use This Book xii\u003c\/p\u003e \u003cp\u003eAcknowledgments xiv\u003c\/p\u003e \u003cp\u003eAbout the Companion Website xiv\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction – Examples from Real Life 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 The Problem of Learning 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Domain 3\u003c\/p\u003e \u003cp\u003e2.2 Range 4\u003c\/p\u003e \u003cp\u003e2.3 Data 4\u003c\/p\u003e \u003cp\u003e2.4 Loss 5\u003c\/p\u003e \u003cp\u003e2.5 Risk 8\u003c\/p\u003e \u003cp\u003e2.6 The Reality of the Unknown Function 12\u003c\/p\u003e \u003cp\u003e2.7 Training and Selection of Models 12\u003c\/p\u003e \u003cp\u003e2.8 Purposes of Learning 14\u003c\/p\u003e \u003cp\u003e2.9 Notation 14\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Regression 15\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 General Framework 16\u003c\/p\u003e \u003cp\u003e3.2 Loss 17\u003c\/p\u003e \u003cp\u003e3.3 Estimating the Model Parameters 17\u003c\/p\u003e \u003cp\u003e3.4 Properties of Fitted Values 19\u003c\/p\u003e \u003cp\u003e3.5 Estimating the Variance 22\u003c\/p\u003e \u003cp\u003e3.6 A Normality Assumption 23\u003c\/p\u003e \u003cp\u003e3.7 Computation 25\u003c\/p\u003e \u003cp\u003e3.8 Categorical Features 26\u003c\/p\u003e \u003cp\u003e3.9 Feature Expansions, Interactions, and Transformations 28\u003c\/p\u003e \u003cp\u003e3.10 Penalized Regression: Model Transformation for Risk Reduction 31\u003c\/p\u003e \u003cp\u003e3.11 Variations in Linear Regression 37\u003c\/p\u003e \u003cp\u003e3.12 Nonlinear Regression 39\u003c\/p\u003e \u003cp\u003e3.13 Nonparametric Regression 42\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Classification 45\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 The Bayes Classifier 46\u003c\/p\u003e \u003cp\u003e4.2 Introduction to Classifiers 47\u003c\/p\u003e \u003cp\u003e4.3 Mitigating Biases in Software, Biases in Data, and Zero Probabilities 49\u003c\/p\u003e \u003cp\u003e4.3.1 Mitigating Biases in Software by Adjusting Loss and Prior Probabilities 50\u003c\/p\u003e \u003cp\u003e4.3.2 Mitigating Biases in Data by Adjusting Loss or Prior Probabilities 51\u003c\/p\u003e \u003cp\u003e4.3.3 Mitigating Effects of Zero-One Probability Estimates 52\u003c\/p\u003e \u003cp\u003e4.4 Class Boundaries 53\u003c\/p\u003e \u003cp\u003e4.5 A Running Example 54\u003c\/p\u003e \u003cp\u003e4.6 Likelihood Methods 55\u003c\/p\u003e \u003cp\u003e4.6.1 Quadratic Discriminant Analysis 56\u003c\/p\u003e \u003cp\u003e4.6.2 Linear Discriminant Analysis 58\u003c\/p\u003e \u003cp\u003e4.6.3 Gaussian Mixture Models 60\u003c\/p\u003e \u003cp\u003e4.6.4 Kernel Density Estimation 61\u003c\/p\u003e \u003cp\u003e4.6.5 Histograms 65\u003c\/p\u003e \u003cp\u003e4.6.6 The Naive Bayes Classifier 68\u003c\/p\u003e \u003cp\u003e4.7 Prototype Methods 69\u003c\/p\u003e \u003cp\u003e4.7.1 k-Nearest-Neighbor 69\u003c\/p\u003e \u003cp\u003e4.7.2 Condensed k-Nearest-Neighbor 72\u003c\/p\u003e \u003cp\u003e4.7.3 Nearest-Cluster 73\u003c\/p\u003e \u003cp\u003e4.7.4 Learning Vector Quantization 73\u003c\/p\u003e \u003cp\u003e4.8 Logistic Regression 76\u003c\/p\u003e \u003cp\u003e4.8.1 The Logistic Regression Model 76\u003c\/p\u003e \u003cp\u003e4.8.2 Adjusting the Marginal or Prior Distribution of Classes 79\u003c\/p\u003e \u003cp\u003e4.8.3 Class Boundaries, Hyperplanes, and Geometry 81\u003c\/p\u003e \u003cp\u003e4.9 Neural Networks 81\u003c\/p\u003e \u003cp\u003e4.9.1 Activation Functions 81\u003c\/p\u003e \u003cp\u003e4.9.2 Neurons 82\u003c\/p\u003e \u003cp\u003e4.9.3 Single-Hidden-Layer Neural Networks 84\u003c\/p\u003e \u003cp\u003e4.9.4 Multi-Hidden-Layer Neural Networks 90\u003c\/p\u003e \u003cp\u003e4.9.5 Adjusting the Marginal or Prior Distribution of Classes 91\u003c\/p\u003e \u003cp\u003e4.9.6 Logistic Regression and Zero-Hidden-Layer Neural Networks 91\u003c\/p\u003e \u003cp\u003e4.10 Classification Trees 93\u003c\/p\u003e \u003cp\u003e4.10.1 Classification of Data by Leaves (Terminal Nodes) 93\u003c\/p\u003e \u003cp\u003e4.10.2 Impurity of Nodes and Trees 94\u003c\/p\u003e \u003cp\u003e4.10.3 Growing Trees 95\u003c\/p\u003e \u003cp\u003e4.10.4 Pruning Trees 98\u003c\/p\u003e \u003cp\u003e4.10.5 Regression Trees 99\u003c\/p\u003e \u003cp\u003e4.11 Support Vector Machines 100\u003c\/p\u003e \u003cp\u003e4.11.1 A Geometric Definition of “Good” 100\u003c\/p\u003e \u003cp\u003e4.11.2 Support Vector Machine Classifiers for Linearly Separable Data 101\u003c\/p\u003e \u003cp\u003e4.11.3 The Central Role of Inner Products 103\u003c\/p\u003e \u003cp\u003e4.11.4 Support Vector Machine Classifiers for Data Not Linearly Separable 104\u003c\/p\u003e \u003cp\u003e4.11.5 Slack Variables as Hinge Loss 105\u003c\/p\u003e \u003cp\u003e4.11.6 Multiple Classes, General Loss, and Non-uniform Class Prior 107\u003c\/p\u003e \u003cp\u003e4.11.7 Approximation of the Bayes Classifier 109\u003c\/p\u003e \u003cp\u003e4.11.8 Inner Products via Kernel Functions 110\u003c\/p\u003e \u003cp\u003e4.12 Postscript: Example Problem Revisited 119\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Bias-Variance Trade-Off 121\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Squared-Error Loss 121\u003c\/p\u003e \u003cp\u003e5.2 General Loss 125\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Combining Classifiers 131\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Ensembles 131\u003c\/p\u003e \u003cp\u003e6.2 Ensemble Design 136\u003c\/p\u003e \u003cp\u003e6.3 Bootstrap Aggregation (Bagging) 138\u003c\/p\u003e \u003cp\u003e6.4 Random Forests 141\u003c\/p\u003e \u003cp\u003e6.5 Boosting and Arcing 142\u003c\/p\u003e \u003cp\u003e6.6 Classification by Regression Ensemble 147\u003c\/p\u003e \u003cp\u003e6.7 Gradient Boosting 151\u003c\/p\u003e \u003cp\u003e6.8 Stacking and Mixture of Experts 156\u003c\/p\u003e \u003cp\u003e6.9 Postscript: Example Problem Revisited 160\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Risk Estimation and Model Selection 163\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Risk Estimation via Training Data 164\u003c\/p\u003e \u003cp\u003e7.2 Risk Estimation via Validation or Test Data 164\u003c\/p\u003e \u003cp\u003e7.2.1 Training, Validation, and Test Data Sets 164\u003c\/p\u003e \u003cp\u003e7.2.2 Training, Validation, and Test Estimates of Risk 165\u003c\/p\u003e \u003cp\u003e7.2.3 Application – Precision of Validation and Test Estimates of Risk 166\u003c\/p\u003e \u003cp\u003e7.2.4 Application – Comparing a Model’s Risk to a Target Value 166\u003c\/p\u003e \u003cp\u003e7.2.5 Application – Comparing the Difference of Models’ Risks to a Target Value 168\u003c\/p\u003e \u003cp\u003e7.3 Cross-Validation 169\u003c\/p\u003e \u003cp\u003e7.4 Improvements on Cross-Validation 171\u003c\/p\u003e \u003cp\u003e7.5 Out-of-Bag Risk Estimation 172\u003c\/p\u003e \u003cp\u003e7.6 Akaike’s Information Criterion 173\u003c\/p\u003e \u003cp\u003e7.7 Schwartz’s Bayesian Information Criterion 174\u003c\/p\u003e \u003cp\u003e7.8 Rissanen’s Minimum Description Length Criterion 175\u003c\/p\u003e \u003cp\u003e7.9 R 2 and Adjusted R 2 175\u003c\/p\u003e \u003cp\u003e7.10 Stepwise Model Selection 177\u003c\/p\u003e \u003cp\u003e7.11 Occam’s Razor 177\u003c\/p\u003e \u003cp\u003e7.12 Size of Validation and Test Data Sets 178\u003c\/p\u003e \u003cp\u003e7.12.1 Measures of Performance for Hypothesis Tests About Risk 178\u003c\/p\u003e \u003cp\u003e7.12.2 Size of Training, Validation, and Test Data Sets 178\u003c\/p\u003e \u003cp\u003e7.12.3 Example Construction of Training, Validation, and Test Data Sets 180\u003c\/p\u003e \u003cp\u003e7.12.4 Example Use of Training and Validation Data Sets 183\u003c\/p\u003e \u003cp\u003e7.12.5 Example Use of Test Data Sets 186\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Consistency 187\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Convergence of Sequences of Random Variables 187\u003c\/p\u003e \u003cp\u003e8.2 Consistency for Parameter Estimation 188\u003c\/p\u003e \u003cp\u003e8.3 Consistency for Prediction 188\u003c\/p\u003e \u003cp\u003e8.4 There Are Consistent and Universally Consistent Classifiers 189\u003c\/p\u003e \u003cp\u003e8.5 Convergence to Asymptopia Is Not Uniform and May Be Slow 191\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Clustering 193\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Gaussian Mixture Models 194\u003c\/p\u003e \u003cp\u003e9.2 k-Means 194\u003c\/p\u003e \u003cp\u003e9.3 Clustering by Mode-Hunting in a Density Estimate 195\u003c\/p\u003e \u003cp\u003e9.4 Using Classifiers to Cluster 196\u003c\/p\u003e \u003cp\u003e9.5 Dissimilarity 196\u003c\/p\u003e \u003cp\u003e9.6 k-Medoids 197\u003c\/p\u003e \u003cp\u003e9.7 k-Modes and k-Prototypes 197\u003c\/p\u003e \u003cp\u003e9.8 Agglomerative Hierarchical Clustering 198\u003c\/p\u003e \u003cp\u003e9.9 Divisive Hierarchical Clustering 199\u003c\/p\u003e \u003cp\u003e9.10 How Many Clusters Are There? Interpretation of Clustering 200\u003c\/p\u003e \u003cp\u003e9.11 An Impossibility Theorem 201\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Optimization 203\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Quasi-Newton Methods 204\u003c\/p\u003e \u003cp\u003e10.1.1 The Newton–Raphson Method for Finding Zeros 204\u003c\/p\u003e \u003cp\u003e10.1.2 The Newton–Raphson Method for Optimization 205\u003c\/p\u003e \u003cp\u003e10.1.3 Gradient Descent 205\u003c\/p\u003e \u003cp\u003e10.1.4 The Broyden–Fletcher–Goldfarb–Shanno Algorithm 205\u003c\/p\u003e \u003cp\u003e10.1.5 Modifications to Quasi-Newton Methods 206\u003c\/p\u003e \u003cp\u003e10.2 The Nelder–Mead Algorithm 207\u003c\/p\u003e \u003cp\u003e10.3 Simulated Annealing 207\u003c\/p\u003e \u003cp\u003e10.4 Genetic Algorithms 209\u003c\/p\u003e \u003cp\u003e10.5 Particle Swarm Optimization 210\u003c\/p\u003e \u003cp\u003e10.6 General Remarks on Optimization 211\u003c\/p\u003e \u003cp\u003e10.6.1 Imperfectly Known Objective Functions 211\u003c\/p\u003e \u003cp\u003e10.6.2 Objective Functions That Are Sums 212\u003c\/p\u003e \u003cp\u003e10.6.3 Optimization from Multiple Starting Points 212\u003c\/p\u003e \u003cp\u003e10.7 Solving Least-Squares Problems via Quasi-Newton Methods 213\u003c\/p\u003e \u003cp\u003e10.8 Gradient Computation for Neural Networks via Backpropagation 214\u003c\/p\u003e \u003cp\u003e10.9 Handling Missing Data via the Expectation-Maximization Algorithm 219\u003c\/p\u003e \u003cp\u003e10.9.1 The General Algorithm 219\u003c\/p\u003e \u003cp\u003e10.9.2 EM Climbs the Marginal Likelihood of the Observations 220\u003c\/p\u003e \u003cp\u003e10.9.3 Example – Fitting a Gaussian Mixture Model via EM 222\u003c\/p\u003e \u003cp\u003e10.9.4 Example – The Expectation Step 223\u003c\/p\u003e \u003cp\u003e10.9.5 Example – The Maximization Step 224\u003c\/p\u003e \u003cp\u003e10.10 Fitting Support Vector Machines via Sequential Minimal Optimization 224\u003c\/p\u003e \u003cp\u003e10.10.1 Primal and Dual Forms of the Linear SVM Optimization Problem 225\u003c\/p\u003e \u003cp\u003e10.10.2 Slater’s Condition and the Karush–Kuhn–Tucker Conditions 226\u003c\/p\u003e \u003cp\u003e10.10.3 Generalization to Kernel Support Vector Machines 228\u003c\/p\u003e \u003cp\u003e10.10.4 Computation of the Intercept 229\u003c\/p\u003e \u003cp\u003e10.10.5 Solving the Dual Problem via Sequential Minimal Optimization 229\u003c\/p\u003e \u003cp\u003e10.10.6 Step 1 – Choosing a Pair of Coordinates to Optimize 230\u003c\/p\u003e \u003cp\u003e10.10.7 Step 2 – Constrained Optimization of a Pair of Coordinates 231\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 High-Dimensional Data 235\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 The Curse of Dimensionality 236\u003c\/p\u003e \u003cp\u003e11.2 Two Running Examples 242\u003c\/p\u003e \u003cp\u003e11.2.1 Example 1: Equilateral Simplex 242\u003c\/p\u003e \u003cp\u003e11.2.2 Example 2: Text 242\u003c\/p\u003e \u003cp\u003e11.3 Reducing Dimension While Preserving Information 243\u003c\/p\u003e \u003cp\u003e11.3.1 The Geometry of Means and Covariances of Real Features 245\u003c\/p\u003e \u003cp\u003e11.3.2 Principal Component Analysis 246\u003c\/p\u003e \u003cp\u003e11.3.3 Working in “Dissimilarity Space” 248\u003c\/p\u003e \u003cp\u003e11.3.4 Linear Multidimensional Scaling 248\u003c\/p\u003e \u003cp\u003e11.3.5 The Singular Value Decomposition and Low-Rank Approximation 250\u003c\/p\u003e \u003cp\u003e11.3.6 Stress-Minimizing Multidimensional Scaling 252\u003c\/p\u003e \u003cp\u003e11.3.7 Projection Pursuit 252\u003c\/p\u003e \u003cp\u003e11.3.8 Feature Selection 253\u003c\/p\u003e \u003cp\u003e11.3.9 Clustering 254\u003c\/p\u003e \u003cp\u003e11.3.10 Manifold Learning 254\u003c\/p\u003e \u003cp\u003e11.3.11 Autoencoders 257\u003c\/p\u003e \u003cp\u003e11.4 Model Regularization 261\u003c\/p\u003e \u003cp\u003e11.4.1 Duality and the Geometry of Parameter Penalization 262\u003c\/p\u003e \u003cp\u003e11.4.2 Parameter Penalization as Prior Information 263\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Communication with Clients 267\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Binary Classification and Hypothesis Testing 267\u003c\/p\u003e \u003cp\u003e12.2 Terminology for Binary Decisions 269\u003c\/p\u003e \u003cp\u003e12.3 Receiver Operating Characteristic (ROC) Curves 271\u003c\/p\u003e \u003cp\u003e12.4 One-Dimensional Measures of Performance 273\u003c\/p\u003e \u003cp\u003e12.5 Confusion Matrices 276\u003c\/p\u003e \u003cp\u003e12.6 Pairwise Model Comparison 277\u003c\/p\u003e \u003cp\u003e12.7 Multiple Testing 277\u003c\/p\u003e \u003cp\u003e12.7.1 Control the Familywise Error 278\u003c\/p\u003e \u003cp\u003e12.7.2 Control the False Discovery Rate 278\u003c\/p\u003e \u003cp\u003e12.8 Expert Systems 279\u003c\/p\u003e \u003cp\u003e12.9 Ethics in Machine Learning 280\u003c\/p\u003e \u003cp\u003e12.9.1 Philosophical Foundations 280\u003c\/p\u003e \u003cp\u003e12.9.2 Clear Goals 281\u003c\/p\u003e \u003cp\u003e12.9.3 Good Practice 281\u003c\/p\u003e \u003cp\u003e12.9.4 Machine Learning Might Not Be the Answer, and That’s OK 282\u003c\/p\u003e \u003cp\u003e12.9.5 Documentation 282\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Current Challenges in Machine Learning 283\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Streaming Data 283\u003c\/p\u003e \u003cp\u003e13.2 Distributed Data 283\u003c\/p\u003e \u003cp\u003e13.3 Semi-Supervised Learning 283\u003c\/p\u003e \u003cp\u003e13.4 Active Learning 284\u003c\/p\u003e \u003cp\u003e13.5 Feature Construction via Deep Neural Networks 284\u003c\/p\u003e \u003cp\u003e13.6 Transfer Learning 284\u003c\/p\u003e \u003cp\u003e13.7 Interpretability and Protection of Complex Models 285\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 R and Python Source Code 287\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Author’s Biases 288\u003c\/p\u003e \u003cp\u003e14.2 Packages and Code 288\u003c\/p\u003e \u003cp\u003e14.3 The Running Example (Section 4.5) 289\u003c\/p\u003e \u003cp\u003e14.4 The Bayes Classifier (Section 4.1) 292\u003c\/p\u003e \u003cp\u003e14.5 Quadratic Discriminant Analysis (Section 4.6.1) 294\u003c\/p\u003e \u003cp\u003e14.6 Linear Discriminant Analysis (Section 4.6.2) 296\u003c\/p\u003e \u003cp\u003e14.7 Gaussian Mixture Models (Section 4.6.3) 297\u003c\/p\u003e \u003cp\u003e14.8 Kernel Density Estimation (Section 4.6.4) 300\u003c\/p\u003e \u003cp\u003e14.9 Histograms (Section 4.6.5) 304\u003c\/p\u003e \u003cp\u003e14.10 The Naive Bayes Classifier (Section 4.6.6) 309\u003c\/p\u003e \u003cp\u003e14.11 k-Nearest-Neighbor (Section 4.7.1) 312\u003c\/p\u003e \u003cp\u003e14.12 Learning Vector Quantization (Section 4.7.4) 314\u003c\/p\u003e \u003cp\u003e14.13 Logistic Regression (Section 4.8) 317\u003c\/p\u003e \u003cp\u003e14.14 Neural Networks (Section 4.9) 319\u003c\/p\u003e \u003cp\u003e14.15 Classification Trees (Section 4.10) 324\u003c\/p\u003e \u003cp\u003e14.16 Support Vector Machines (Section 4.11) 332\u003c\/p\u003e \u003cp\u003e14.17 Bootstrap Aggregation (Bagging) (Section 6.3) 341\u003c\/p\u003e \u003cp\u003e14.18 Random Forests (Section 6.4) 343\u003c\/p\u003e \u003cp\u003e14.19 Boosting by Reweighting (Section 6.5) 345\u003c\/p\u003e \u003cp\u003e14.20 Boosting by Sampling (Arcing) (Section 6.5) 346\u003c\/p\u003e \u003cp\u003e14.21 Gradient Boosted Trees (Section 6.7) 347\u003c\/p\u003e \u003cp\u003eAppendix-A: List of Symbols 351\u003c\/p\u003e \u003cp\u003eAppendix-B: The Condition Number of a Matrix with Respect to a Norm 353\u003c\/p\u003e \u003cp\u003eAppendix-C: Converting Between Normal Parameters and Level-Curve Ellipsoids 357\u003c\/p\u003e \u003cp\u003eAppendix-D: The Geometry of Linear Functions and Linear Classifiers 359\u003c\/p\u003e \u003cp\u003eAppendix-E: Training Data and Fitted Parameters 367\u003c\/p\u003e \u003cp\u003eAppendix-F: Solutions to Selected Exercises 371\u003c\/p\u003e \u003cp\u003eBibliography 399\u003c\/p\u003e \u003cp\u003eIndex 413\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Mathematics [\u003ca title=\"See our other books on Mathematics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mathematics%20%5BPB%5D%22\"\u003ePB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand New","offer_id":52433751343384,"sku":"9781394325252","price":68.79,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394325252.jpg?v=1784853693","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/machine-learning-a-concise-introduction-hardback-9781394325252","provider":"Freshly Printed Books","version":"1.0","type":"link"}