An Information-Theoretic Approach to Neural Computing

Gustavo Deco|Dragan Obradovic

ISBN 10: 0387946667 / ISBN 13: 9780387946665

Published by Springer New York, 1996

About this Item

Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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.

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Bibliographic Details

• Title: An Information-Theoretic Approach to Neural Computing
• Publisher: Springer New York
• Publication Date: 1996
• Language: English
• ISBN 10: 0387946667
• ISBN 13: 9780387946665
• Binding: Hardcover
• Condition: New

About this title

Synopsis

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.

From the Back Cover

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 nonlinear independent component analysis, and Boltzmann machines. Readers are assumed to have a basic understanding of neural networks, but all of 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 book to be a very valuable contribution to this topic.