Perspectives in Neural Computing

By: Gustavo Deco, Dragan Obradovic

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Hardcover | 5 September 1997

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284 Pages

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24.77 x 16.51 x 1.91

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Neural networks provide a powerful new technology to model and control nonlinear and complex systems. In this book, the authors present a detailed formulation of neural networks from the information-theoretic viewpoint. They show how this perspective provides new insights into the design theory of neural networks. In particular, they show how these methods may be applied to the topics of supervised and unsupervised learning, including feature extraction, linear and nonlinear independent component analysis, and Boltzmann machines.
Readers are assumed to have a basic understanding of neural networks, but all of the relevant concepts from information theory are carefully introduced and explained. Consequently, readers from several different scientific disciplines - notably, cognitive scientists, engineers, physicists, statisticians, and computer scientists - will find this book to be a very valuable contribution to this topic.

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Science Artificial Intelligence Neural Networks & Fuzzy Systems Mathematical Theory of Computation

Acknowledgments
Foreword
Introduction	p. 1
Preliminaries of Information Theory and Neural Networks	p. 7
Linear Feature Extraction: Infomax Principle	p. 41
Independent Component Analysis: General Formulation and Linear Case	p. 65
Nonlinear Feature Extraction: Boolean Stochastic Networks	p. 109
Nonlinear Feature Extraction: Deterministic Neural Networks	p. 135
Supervised Learning and Statistical Estimation	p. 169
Statistical Physics Theory of Supervised Learning and Generalization	p. 187
Composite Networks	p. 219
Information Theory Based Regularizing Methods	p. 225
References	p. 243
Index	p. 259
Table of Contents provided by Blackwell. All Rights Reserved.

Perspectives in Neural Computing

At a Glance

Hardcover

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How to return your order

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