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Więcej informacji o Principal Component Neural Networks - Theory and Applications

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Opis

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.

Szczegóły książki

Kategoria Knihy po anglicky Computing & information technology Computer science Artificial intelligence

• Pełny tytuł: Principal Component Neural Networks - Theory and Applications
• Autor: K. I. Diamantaras, S. Y. Kung
• Język: Angielski
• Oprawa: Twarda
• Liczba stron: 272
• EAN: 9780471054368
• ISBN: 0471054364
• ID: 04887319
• Wydawca: John Wiley & Sons Inc
• Waga: 556 g
• Wymiary: 244 × 166 × 20 mm
• Data wydania: 04. April 1996

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