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An Information-Theoretic Approach to Neural Computing
     

An Information-Theoretic Approach to Neural Computing

by Gustavo Deco, Dragan Obradovic
 
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

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:
9781461284697
Publisher:
Springer New York
Publication date:
07/31/2012
Series:
Perspectives in Neural Computing Series
Edition description:
Softcover reprint of the original 1st ed. 1996
Pages:
262
Product dimensions:
6.10(w) x 9.25(h) x 0.02(d)

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