Features
- Cover Type: Hard Cover with 272 pages
- Published by: Wiley-Interscience February 1996
- Written in: English
- ISBN 10 Number: 0471054364
- ISBN 13 Number: 978-0471054368
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Book Dimensions:
9.5 x 6.4 x 0.7 inches
- Weighs: 1.2 pounds
Product Description
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. looks at the principles of biological perceptual systems to explain how the brain works. Every chapter contains a selected list of applications examples from diverse areas.
Book Info
Focuses on issues pertaining to both neural network models and theoretical extensions of PCA. Valuable to researchers and advanced students in neural network theory and signal processing. DLC: Neural networks (Computer science)
Reader Reviews
This book explains the application of Neural Networks into Principal Component Analysis with clear logic and easy examples. The only problem is that I still think this book is too succinct. I'll have to find papers to see the updating rule for some parameters. But as the first step to understand this topic, this book is good enough.
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