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An Information-Theoretic Approach to Neural Computing / Edition 1

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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.

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Product Details

  • ISBN-13: 9780387946665
  • Publisher: Springer New York
  • Publication date: 2/8/1996
  • Series: Perspectives in Neural Computing Series
  • Edition description: 1st ed. 1996. Corr. 2nd printing 1997
  • Edition number: 1
  • Pages: 276
  • Product dimensions: 9.21 (w) x 6.14 (h) x 0.69 (d)

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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