Principal Component Neural Networks - Diamantaras, K. I.;Kung, S. Y.
226,99 €
inkl. MwSt.
Versandkostenfrei*
Versandfertig in über 4 Wochen
payback
113 °P sammeln
  • Gebundenes Buch

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.…mehr

Andere Kunden interessierten sich auch für
Produktbeschreibung
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.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Autorenporträt
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.