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An Information-Theoretic Approach to Neural Computing

  • Book
  • © 1996

Overview

Authors:
  1. Gustavo Deco

    1. Corporate Research and Development, Siemens AG, Munich, Germany

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    You can also search for this author in PubMed   Google Scholar

  2. Dragan Obradovic

    1. Corporate Research and Development, Siemens AG, Munich, Germany

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    You can also search for this author in PubMed   Google Scholar

Part of the book series: Perspectives in Neural Computing (PERSPECT.NEURAL)

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-xiii
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  2. Introduction

    1. Introduction

      • Gustavo Deco, Dragan Obradovic
      Pages 1-5

  3. Preliminaries of Information Theory and Neural Networks

    1. Preliminaries of Information Theory and Neural Networks

      • Gustavo Deco, Dragan Obradovic
      Pages 7-37

  4. Unsupervised Learning

    1. Front Matter

      Pages 39-39
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    2. Linear Feature Extraction: Infomax Principle

      • Gustavo Deco, Dragan Obradovic
      Pages 41-63

    3. Independent Component Analysis: General Formulation and Linear Case

      • Gustavo Deco, Dragan Obradovic
      Pages 65-107

    4. Nonlinear Feature Extraction: Boolean Stochastic Networks

      • Gustavo Deco, Dragan Obradovic
      Pages 109-133

    5. Nonlinear Feature Extraction: Deterministic Neural Networks

      • Gustavo Deco, Dragan Obradovic
      Pages 135-166

  5. Supervised Learning

    1. Front Matter

      Pages 167-167
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    2. Supervised Learning and Statistical Estimation

      • Gustavo Deco, Dragan Obradovic
      Pages 169-186

    3. Statistical Physics Theory of Supervised Learning and Generalization

      • Gustavo Deco, Dragan Obradovic
      Pages 187-217

    4. Composite Networks

      • Gustavo Deco, Dragan Obradovic
      Pages 219-224

    5. Information Theory Based Regularizing Methods

      • Gustavo Deco, Dragan Obradovic
      Pages 225-241

  6. Back Matter

    Pages 243-261
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Keywords

About this book

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 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 several different scientific disciplines, notably cognitive scientists, engineers, physicists, statisticians, and computer scientists, will find this to be a very valuable introduction to this topic.

Authors and Affiliations

  • Corporate Research and Development, Siemens AG, Munich, Germany

    Gustavo Deco, Dragan Obradovic

Bibliographic Information

  • Book Title: An Information-Theoretic Approach to Neural Computing

  • Authors: Gustavo Deco, Dragan Obradovic

  • Series Title: Perspectives in Neural Computing

  • DOI: https://doi.org/10.1007/978-1-4612-4016-7

  • Publisher: Springer New York, NY

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer-Verlag New York, Inc. 1996

  • Hardcover ISBN: 978-0-387-94666-5Published: 08 February 1996

  • Softcover ISBN: 978-1-4612-8469-7Published: 17 September 2011

  • eBook ISBN: 978-1-4612-4016-7Published: 06 December 2012

  • Series ISSN: 1431-6854

  • Edition Number: 1

  • Number of Pages: XIV, 262

  • Topics: Artificial Intelligence

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