Principal Component Neural Networks : Theory and Applications (Adaptive and Learning Systems for Signal Processing, Communications and Control Series)
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| Автор | | K. I. Diamantaras, S. Y. Kung |
| Издатель | | Thomson Course Technology |
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Содержание:
Общедоступная история астрономии в XIX столетии
Описание:
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.Похожие книги
