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Principal Component Neural Networks

Theory and Applications

Inbunden, Engelska, 1996

Av K. I. Diamantaras, S. Y. Kung, S. y. Kung, Diamantaras, Kung, K I Diamantaras, S Y Kung

2 789 kr

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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.

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