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Item Details
Title: AN INFORMATION-THEORETIC APPROACH TO NEURAL COMPUTING
By: Gustavo Deco, Dragan Obradovic
Format: Paperback

List price: £44.99


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ISBN 10: 1461284694
ISBN 13: 9781461284697
Publisher: SPRINGER-VERLAG NEW YORK INC.
Pub. date: 17 September, 2011
Edition: Softcover reprint of the original 1st ed. 1996
Series: Perspectives in Neural Computing
Pages: 262
Synopsis: 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.
Illustrations: biography
Publication: US
Imprint: Springer-Verlag New York Inc.
Returns: Returnable
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