Principal Component Neural Networks : Theory and Applications

K I Diamantaras

ISBN 10: 0471054364 / ISBN 13: 9780471054368

Edité par Wiley Mär 1996, 1996

A propos de cet article

Neuware - 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.

N° de réf. du vendeur 9780471054368

Détails bibliographiques

• Titre: Principal Component Neural Networks : Theory and Applications
• Éditeur: Wiley Mär 1996
• Date de parution: 1996
• Langue: anglais
• ISBN à 10 chiffres: 0471054364
• ISBN à 13 chiffres: 9780471054368
• Reliure: Buch
• État de l'article: Neu
• Dimensions: 240 L x 161 H x 19 P (en millimètres) 240 millimètres de largeur par 161 millimètres de hauteur par 19 millimètres de profondeur
• Poids: 572 grammes

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.