Principal Component Neural Networks: Theory and Applications

Kung, S. Y.,Diamantaras, K. I.

ISBN 10: 0471054364 / ISBN 13: 9780471054368

Published by Wiley-Interscience, 1996

About this Item

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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: Good

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.