Principal Component Neural Networks: Theory and Applications

✍ Scribed by K. I. Diamantaras, S. Y. Kung

Publisher: Wiley-Interscience
Year: 1996
Tongue: English
Leaves: 272
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ 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.

✦ Subjects

Информатика и вычислительная техника;Искусственный интеллект;Нейронные сети;

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