Principal Component Neural Networks Theory and Applications
K. I. Diamantaras/ S. Y. Kung
ISBN 10: 0471054364 ISBN 13: 9780471054368
Published by Wiley-Interscience, 1996
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1st edition. 272 pages. 9.75x6.50x0.75 inches. In Stock. This item is printed on demand. Seller Inventory # __0471054364
Synopsis:
Systematically explores the relationship between principal component analysis (PCA) and neural networks. Provides a synergistic examination of the mathematical, algorithmic, application and architectural aspects of principal component neural networks. Using a unified formulation, the authors present neural models performing PCA from the Hebbian learning rule and those which use least squares learning rules such as back-propagation. Examines the principles of biological perceptual systems to explain how the brain works. Every chapter contains a selected list of applications examples from diverse areas.
About the Author:
K. I. Diamantaras is a research scientist at Aristotle University in Thessaloniki, Greece. He received his PhD from Princeton University and was formerly a research scientist for Siemans Corporate Research.
S. Y. Kung is Professor of Electrical Engineering at Princeton University and received his PhD from Stanford University. He was formerly a professor of electrical engineering at the University of Southern California.
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Bibliographic Details
Title: Principal Component Neural Networks Theory...
Publisher: Wiley-Interscience
Publication Date: 1996
Binding: Hardcover
Condition: Brand New
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