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Item Details
| Title:
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AN INFORMATION-THEORETIC APPROACH TO NEURAL COMPUTING
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| By: |
Gustavo Deco, D. Obradovic |
| Format: |
Hardback |
| List price:
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£109.99 |
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We currently do not stock this item, please contact the publisher directly for
further information.
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| ISBN 10: |
0387946667 |
| ISBN 13: |
9780387946665 |
| Publisher: |
SPRINGER-VERLAG NEW YORK INC. |
| Pub. date: |
5 September, 1997 |
| Edition: |
1st ed. 1996. Corr. 2nd printing 1997 |
| Series: |
Perspectives in Neural Computing |
| Pages: |
262 |
| Description: |
Neural networks provide powerful technology to model and control nonlinear and complex systems. This book presents a detailed formulation of neural networks from the information-theoretic viewpoint, showing how this perspective provides insights into the design theory of neural networks. |
| 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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