An Information-Theoretic Approach to Neural Computing

Author:   Gustavo Deco ,  Dragan Obradovic
Publisher:   Springer-Verlag New York Inc.
Edition:   1st ed. 1996. Corr. 2nd printing 1997
ISBN:  

9780387946665


Pages:   262
Publication Date:   08 February 1996
Format:   Hardback
Availability:   In Print   Availability explained
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An Information-Theoretic Approach to Neural Computing


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Author:   Gustavo Deco ,  Dragan Obradovic
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   1st ed. 1996. Corr. 2nd printing 1997
Dimensions:   Width: 15.50cm , Height: 1.70cm , Length: 23.50cm
Weight:   1.270kg
ISBN:  

9780387946665


ISBN 10:   0387946667
Pages:   262
Publication Date:   08 February 1996
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

1 Introduction.- 2 Preliminaries of Information Theory and Neural Networks.- 2.1 Elements of Information Theory.- 2.2 Elements of the Theory of Neural Networks.- I: Unsupervised Learning.- 3 Linear Feature Extraction: Infomax Principle.- 4 Independent Component Analysis: General Formulation and Linear Case.- 5 Nonlinear Feature Extraction: Boolean Stochastic Networks.- 6 Nonlinear Feature Extraction: Deterministic Neural Networks.- II: Supervised Learning.- 7 Supervised Learning and Statistical Estimation.- 8 Statistical Physics Theory of Supervised Learning and Generalization.- 9 Composite Networks.- 10 Information Theory Based Regularizing Methods.- References.

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