
Principal Component Neural Networks
Theory and Applications
Wiley (Publisher)
Published on 4. April 1996
Book
Hardback
XIV, 258 pages
978-0-471-05436-8 (ISBN)
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. 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.
More details
Product info
gebunden
Series
Edition
1. Auflage
Language
English
Place of publication
United States
Publishing group
John Wiley & Sons Inc
Target group
College/higher education
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
Dimensions
Height: 240 mm
Width: 161 mm
Thickness: 19 mm
Weight
572 gr
ISBN-13
978-0-471-05436-8 (9780471054368)
Schweitzer Classification
Persons
K. I. Diamantaras is a research scientist at Aristotle University in Thessaloniki, Greece. He received his PhD from Princeton University and was formerly a research scientist for Siemans Corporate Research.
S. Y. Kung is Professor of Electrical Engineering at Princeton University and received his PhD from Stanford University. He was formerly a professor of electrical engineering at the University of Southern California.
Content
A Review of Linear Algebra.
Principal Component Analysis.
PCA Neural Networks.
Channel Noise and Hidden Units.
Heteroassociative Models.
Signal Enhancement Against Noise.
VLSI Implementation.
Appendices.
Bibliography.
Index.