Description
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.
Book Information
ISBN 9780471054368
Author K. I. Diamantaras
Format Hardback
Page Count 272
Imprint Wiley-Interscience
Publisher John Wiley & Sons Inc
Weight(grams) 567g
Dimensions(mm) 241mm * 161mm * 20mm
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.
Book Information
ISBN 9780471054368
Author K. I. Diamantaras
Format Hardback
Page Count 272
Imprint Wiley-Interscience
Publisher John Wiley & Sons Inc
Weight(grams) 567g
Dimensions(mm) 241mm * 161mm * 20mm
