Principal Component Neural Networks Theory and Applications

K. I. Diamantaras/ S. Y. Kung

ISBN 10: 0471054364 / ISBN 13: 9780471054368

Published by Wiley-Interscience, 1996

About this Item

1st edition. 272 pages. 9.75x6.50x0.75 inches. In Stock. This item is printed on demand.

Seller Inventory # __0471054364

Bibliographic Details

• Title: Principal Component Neural Networks Theory and Applications
• Publisher: Wiley-Interscience
• Publication Date: 1996
• Language: English
• ISBN 10: 0471054364
• ISBN 13: 9780471054368
• Binding: Hardcover
• Condition: Brand New
• Weight: 0.48 kilograms

About this title

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.