Principal component neural networks : theory and applications / K.I. Diamantaras, S.Y. Kung.
"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 p...
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| Main Authors: | , |
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| Format: | Book |
| Language: | English |
| Published: |
New York :
Wiley,
[1996]
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| Series: | Adaptive and learning systems for signal processing, communications, and control.
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| Subjects: | |
| Online Access: | Contributor biographical information |
| Summary: | "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."--Publisher description. |
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| Item Description: | "A Wiley-Interscience publication.". |
| Physical Description: | xii, 255 pages : illustrations ; 24 cm. |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 0471054364 9780471054368 |