Neural Network Learning: Theoretical Foundations

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Cambridge University Press, Aug 20, 2009 - Computers - 404 pages
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This important work describes recent 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. Chapters survey research on pattern classification with binary-output networks, including a discussion of the relevance of the Vapnik Chervonenkis dimension, and of estimates of the dimension for several neural network models. In addition, Anthony and Bartlett develop a model of classification by real-output networks, and demonstrate the usefulness of classification with a "large margin." The authors explain the role of scale-sensitive versions of the Vapnik Chervonenkis dimension in large margin classification, and in real prediction. Key chapters 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 accessible to researchers and graduate students in computer science, engineering, and mathematics.
  

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Contents

1 Introduction
1
Pattern Classification with BinaryOutput Neural Networks
11
Pattern Classification with RealOutput Networks
131
Learning RealValued Functions
229
Algorithmics
297
Appendix 1 Useful Results
357
Bibliography
365
Author index 3
379
Subject index
382
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About the author (2009)

Martin Anthony is Professor of Mathematics at the London School of Economics (LSE) and Academic Co-ordinator for Mathematics on the University of London International Programmes for which LSE has academic oversight. He has over 20 years' experience of teaching students at all levels of university and is the author of four books, including the textbook Mathematics for Economics and Finance: Methods and Modelling (Cambridge University Press, 1996). He also has extensive experience of preparing distance learning materials.

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