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Principal Component Neural Networks : Theory and Applications, Hardback Book

Hardback

Description

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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