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...
Saved in:
| 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 |
MARC
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| 100 | 1 | |a Diamantaras, Konstantinos I. |e author. |9 270854 | |
| 245 | 1 | 0 | |a Principal component neural networks : |b theory and applications / |c K.I. Diamantaras, S.Y. Kung. |
| 264 | 1 | |a New York : |b Wiley, |c [1996] | |
| 264 | 4 | |c ©1996 | |
| 300 | |a xii, 255 pages : |b illustrations ; |c 24 cm. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a unmediated |b n |2 rdamedia | ||
| 338 | |a volume |b nc |2 rdacarrier | ||
| 490 | 1 | |a Adaptive and learning systems for signal processing, communications, and control | |
| 500 | |a "A Wiley-Interscience publication.". | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | 0 | |t Preface -- |g 1. |t Introduction -- |g 2. |t A Review of Linear Algebra -- |g 3. |t Principal Component Analysis -- |g 4. |t PCA Neural Networks -- |g 5. |t Channel Noise and Hidden Units -- |g 6. |t Heteroassociative Models -- |g 7. |t Signal Enhancement Against Noise -- |g 8. |t VLSI Implementation -- |t Appendix A Stochastic Approximation -- |t Appendix B Derivatives with Vectors and Matrices -- |t Appendix C Compactness and Convexity -- |t Bibliography -- |t Index. |
| 520 | |a "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. | ||
| 588 | |a Machine converted from AACR2 source record. | ||
| 650 | 0 | |a Neural networks (Computer science) |9 327371 | |
| 700 | 1 | |a Kung, S. Y. |q (Sun Yuan) |e author. |9 257025 | |
| 830 | 0 | |a Adaptive and learning systems for signal processing, communications, and control. |9 239363 | |
| 856 | 4 | 2 | |3 Contributor biographical information |u http://www.loc.gov/catdir/bios/wiley041/95000242.html |
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