An Information-Theoretic Approach to Neural Computing / Edition 1

An Information-Theoretic Approach to Neural Computing / Edition 1

by Gustavo Deco, Dragan Obradovic
     
 

ISBN-10: 0387946667

ISBN-13: 9780387946665

Pub. Date: 02/08/1996

Publisher: Springer New York

A detailed formulation of neural networks from the information-theoretic viewpoint. The authors show how this perspective provides new insights into the design theory of neural networks. In particular they demonstrate how these methods may be applied to the topics of supervised and unsupervised learning, including feature extraction, linear and non-linear

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Overview

A detailed formulation of neural networks from the information-theoretic viewpoint. The authors show how this perspective provides new insights into the design theory of neural networks. In particular they demonstrate how these methods may be applied to the topics of supervised and unsupervised learning, including feature extraction, linear and non-linear independent component analysis, and Boltzmann machines. Readers are assumed to have a basic understanding of neural networks, but all the relevant concepts from information theory are carefully introduced and explained. Consequently, readers from varied scientific disciplines, notably cognitive scientists, engineers, physicists, statisticians, and computer scientists, will find this an extremely valuable introduction to this topic.

Product Details

ISBN-13:
9780387946665
Publisher:
Springer New York
Publication date:
02/08/1996
Series:
Perspectives in Neural Computing Series
Edition description:
1st ed. 1996. Corr. 2nd printing 1997
Pages:
276
Product dimensions:
6.10(w) x 9.25(h) x 0.03(d)

Related Subjects

Table of Contents

Contents: Introduction.- Preliminaries of Information Theory and Neural Networks.- Linear Feature Extraction: Infomax Principle.- Independent Component Analysis: General Formulation and Linear Case.- Nonlinear Feature Extraction: Boolean Shastic Networks.- Nonlinear Feature Extraction: Deterministic Neural Networks.- Supervised Learning and Statistical Estimation.- Statistical Physics Theory of Supervised Learning and Generalization.- Composite Networks.- Information Theory Based Regularizing Methods.

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