| An Information-Theoretic Approach to Neural Computing 1996. Corr. 2nd Edition Contributor(s): Deco, Gustavo (Author), Obradovic, Dragan (Author) |
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ISBN: 0387946667 ISBN-13: 9780387946665 Publisher: Springer OUR PRICE: $104.49 Product Type: Hardcover - Other Formats Published: February 1996 Annotation: 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. |
| Additional Information |
| BISAC Categories: - Computers | Neural Networks |
| Dewey: 006.3 |
| LCCN: 95048306 |
| Series: Perspectives in Neural Computing |
| Physical Information: 0.82" H x 6.43" W x 9.55" (1.27 lbs) 262 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: 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. |