Principal Component Neural Networks: Theory and Applications - Couverture rigide

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

Couverture rigide

ISBN 10 : 0471054364 ISBN 13 : 9780471054368

Editeur : Wiley-Interscience, 1996

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.

A propos du livre

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.

Détails de l'édition

• Éditeur: Wiley-Interscience
• Date d'édition: 1996
• Langue: anglais
• ISBN 10: 0471054364
• ISBN 13: 9780471054368
• Reliure: Relié
• Nombre de pages: 272
• Coordonnées du fabricant: Wiley VCH Verlag GmbH; info@wiley-vch.de; ; Boschstraße 12; Weinheim; Baden-Württemberg; 69469; Allemagne