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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.
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1
An Information-Theoretic Approach to Neural Computing
Sep 30, 2011, Springer
paperback
1461240174 9781461240174
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2
An Information-Theoretic Approach to Neural Computing (Perspectives in Neural Computing)
September 5, 1997, Springer
in English
0387946667 9780387946665
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3
An information-theoretic approach to neural computing
1996, Springer
in English
0387946667 9780387946665
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4
An information-theoretic approach to neural computing
1996, Springer
in English
0038794667 9780038794669
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Feedback?July 29, 2024 | Edited by MARC Bot | import existing book |
February 26, 2022 | Edited by ImportBot | import existing book |
July 30, 2019 | Edited by MARC Bot | associate edition with work OL2979565W |
April 28, 2010 | Edited by Open Library Bot | Linked existing covers to the work. |
December 10, 2009 | Created by WorkBot | add works page |