{"product_id":"neural-network-learning-theoretical-foundations-hardback-9780521573535","title":"Neural Network Learning; Theoretical Foundations (Hardback) 9780521573535","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eNeural Network Learning\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eTheoretical Foundations\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cem\u003eThis book describes theoretical advances in the study of artificial neural networks.\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eMartin Anthony (Author), Peter L. Bartlett (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780521573535, Cambridge University Press\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 4 November 1999\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e404 pages\u003cbr\u003e22.9 x 15.2 x 2.7 cm, 0.76 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 is a useful and readable mongraph. For beginners it is a nice introduction to the subject, for experts a valuable reference.' Zentralblatt MATH\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eThis book describes theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. Research on pattern classification with binary-output networks is surveyed, including a discussion of the relevance of the Vapnik–Chervonenkis dimension, and calculating estimates of the dimension for several neural network models. A model of classification by real-output networks is developed, and the usefulness of classification with a 'large margin' is demonstrated. The authors explain the role of scale-sensitive versions of the Vapnik–Chervonenkis dimension in large margin classification, and in real prediction. They also discuss the computational complexity of neural network learning, describing a variety of hardness results, and outlining two efficient constructive learning algorithms. The book is self-contained and is intended to be accessible to researchers and graduate students in computer science, engineering, and mathematics.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e1. Introduction\u003cbr\u003e Part I. Pattern Recognition with Binary-output Neural Networks: 2. The pattern recognition problem\u003cbr\u003e 3. The growth function and VC-dimension\u003cbr\u003e 4. General upper bounds on sample complexity\u003cbr\u003e 5. General lower bounds\u003cbr\u003e 6. The VC-dimension of linear threshold networks\u003cbr\u003e 7. Bounding the VC-dimension using geometric techniques\u003cbr\u003e 8. VC-dimension bounds for neural networks\u003cbr\u003e Part II. Pattern Recognition with Real-output Neural Networks: 9. Classification with real values\u003cbr\u003e 10. Covering numbers and uniform convergence\u003cbr\u003e 11. The pseudo-dimension and fat-shattering dimension\u003cbr\u003e 12. Bounding covering numbers with dimensions\u003cbr\u003e 13. The sample complexity of classification learning\u003cbr\u003e 14. The dimensions of neural networks\u003cbr\u003e 15. Model selection\u003cbr\u003e Part III. Learning Real-Valued Functions: 16. Learning classes of real functions\u003cbr\u003e 17. Uniform convergence results for real function classes\u003cbr\u003e 18. Bounding covering numbers\u003cbr\u003e 19. The sample complexity of learning function classes\u003cbr\u003e 20. Convex classes\u003cbr\u003e 21. Other learning problems\u003cbr\u003e Part IV. Algorithmics: 22. Efficient learning\u003cbr\u003e 23. Learning as optimisation\u003cbr\u003e 24. The Boolean perceptron\u003cbr\u003e 25. Hardness results for feed-forward networks\u003cbr\u003e 26. Constructive learning algorithms for two-layered networks.\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: 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]\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":46066003575064,"sku":"9780521573535","price":109.59,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780521118620.jpg?v=1691408724","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/neural-network-learning-theoretical-foundations-hardback-9780521573535","provider":"Freshly Printed Books","version":"1.0","type":"link"}