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.
- ISBN13 9780470301050
- Publish Date 21 April 2008 (first published 4 April 1996)
- Publish Status Active
- Publish Country US
- Imprint Wiley-Interscience
- Format eBook
- Pages 272
- Language English
- URL http://wiley.com