Principal Component Neural Networks: Theory and Applications
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- Wiley
More About This Title Principal Component Neural Networks: Theory and Applications
English
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.
English
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.
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.
English
A Review of Linear Algebra.
Principal Component Analysis.
PCA Neural Networks.
Channel Noise and Hidden Units.
Heteroassociative Models.
Signal Enhancement Against Noise.
VLSI Implementation.
Appendices.
Bibliography.
Index.
Principal Component Analysis.
PCA Neural Networks.
Channel Noise and Hidden Units.
Heteroassociative Models.
Signal Enhancement Against Noise.
VLSI Implementation.
Appendices.
Bibliography.
Index.
