Principal Component Neural Networks Theory and Applications

K. I. Diamantaras/ S. Y. Kung

ISBN 10: 0471054364 / ISBN 13: 9780471054368

Edité par Wiley-Interscience, 1996

A propos de cet article

1st edition. 272 pages. 9.75x6.50x0.75 inches. In Stock. N° de réf. du vendeur x-0471054364

Détails bibliographiques

Titre : Principal Component Neural Networks Theory...

Éditeur : Wiley-Interscience

Date d'édition : 1996

Reliure : Hardcover

Etat : Brand New

A propos de ce titre

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.

À propos de l?auteur:

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.