Principal Component Neural Networks Theory and Applications

K. I. Diamantaras/ S. Y. Kung

ISBN 10: 0471054364 / ISBN 13: 9780471054368

Editore: Wiley-Interscience, 1996

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1st edition. 272 pages. 9.75x6.50x0.75 inches. In Stock.

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Dati bibliografici

• Titolo: Principal Component Neural Networks Theory and Applications
• Editore: Wiley-Interscience
• Data di pubblicazione: 1996
• Lingua: Inglese
• ISBN 10: 0471054364
• ISBN 13: 9780471054368
• Rilegatura: Hardcover
• Condizione: Brand New

Descrizione articolo

Riassunto

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.

Informazioni sull?autore

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.