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An Information-Theoretic Approach to Neural Computing

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Cover of 'An Information-Theoretic Approach to Neural Computing'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction
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    Chapter 2 Preliminaries of Information Theory and Neural Networks
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    Chapter 3 Linear Feature Extraction: Infomax Principle
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    Chapter 4 Independent Component Analysis: General Formulation and Linear Case
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    Chapter 5 Nonlinear Feature Extraction: Boolean Stochastic Networks
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    Chapter 6 Nonlinear Feature Extraction: Deterministic Neural Networks
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    Chapter 7 Supervised Learning and Statistical Estimation
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    Chapter 8 Statistical Physics Theory of Supervised Learning and Generalization
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    Chapter 9 Composite Networks
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    Chapter 10 Information Theory Based Regularizing Methods
Attention for Chapter 8: Statistical Physics Theory of Supervised Learning and Generalization
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Chapter title
Statistical Physics Theory of Supervised Learning and Generalization
Chapter number 8
Book title
An Information-Theoretic Approach to Neural Computing
Published by
Springer, New York, NY, January 1996
DOI 10.1007/978-1-4612-4016-7_8
Book ISBNs
978-1-4612-8469-7, 978-1-4612-4016-7
Authors

Gustavo Deco, Dragan Obradovic

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