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Other titles in the Adaptive and Learning Systems for Signal Processing, Communications and Control series:

Principal Component Neural Networks: Theory and Applications (Adaptive and Learning Systems for Signal Processing, Communications and Control)

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Principal Component Neural Networks: Theory and Applications (Adaptive and Learning Systems for Signal Processing, Communications and Control) Cover

ISBN13: 9780471054368
ISBN10: 0471054364
Condition:
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Synopses & Reviews

Publisher Comments:

Principal Component Neural Networks Theory and Applications

Understanding the underlying principles of biological perceptual systems is of vital importance not only to neuroscientists, but, increasingly, to engineers and computer scientists who wish to develop artificial perceptual systems. In this original and groundbreaking work, the authors systematically examine the relationship between the powerful technique of Principal Component Analysis (PCA) and neural networks. Principal Component Neural Networks focuses on issues pertaining to both neural network models (i.e., network structures and algorithms) and theoretical extensions of PCA. In addition, it provides basic review material in mathematics and neurobiology. This book presents neural models originating from both the Hebbian learning rule and least squares learning rules, such as back-propagation. Its ultimate objective is to provide a synergistic exploration of the mathematical, algorithmic, application, and architectural aspects of principal component neural networks. Especially valuable to researchers and advanced students in neural network theory and signal processing, this book offers application examples from a variety of areas, including high-resolution spectral estimation, system identification, image compression, and pattern recognition.

Book News Annotation:

Explicates the relationship between principle component analysis (PCA) and neural networks, studying issues pertaining to both neural network models, such as network structures and algorithms, and theoretical extensions/generalizations of PCA. Includes reference mathematical appendices. Of interest to readers with background in college calculus and probability theory, in disciplines such as mathematics; statistics; neuropsychology; artificial intelligence; and engineering.
Annotation c. Book News, Inc., Portland, OR (booknews.com)

Synopsis:

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.

About the Author

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.

Table of Contents

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.

What Our Readers Are Saying

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trungkienkaka, October 8, 2013 (view all comments by trungkienkaka)
amazing book. it's very useful for me
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Product Details

ISBN:
9780471054368
With:
Kung, S. Y.
Author:
Diamantaras, Konstantinos I.
Author:
Diamantaras, Kostas
Author:
Diamantaras, K. I.
Author:
Kung, S. Y.
Publisher:
Wiley-Interscience
Location:
New York :
Subject:
Engineering - Electrical & Electronic
Subject:
Artificial Intelligence
Subject:
Neural Networks
Subject:
Neural networks (computer science)
Subject:
Artificial Intelligence - General
Subject:
Electricity
Subject:
Intelligence (AI) & Semantics
Subject:
Networking - General
Copyright:
Edition Description:
Includes bibliographical references and index.
Series:
Adaptive and Learning Systems for Signal Processing, Communications and Control Series
Series Volume:
4
Publication Date:
19960308
Binding:
HARDCOVER
Grade Level:
General/trade
Language:
English
Illustrations:
Yes
Pages:
272
Dimensions:
241 x 161 x 20 mm 20 oz

Related Subjects

Computers and Internet » Artificial Intelligence » General
Computers and Internet » Computers Reference » General
Computers and Internet » Networking » General
Health and Self-Help » Health and Medicine » Medical Specialties
Science and Mathematics » Chemistry » General
Science and Mathematics » Chemistry » Organic
Science and Mathematics » Electricity » General Electricity

Principal Component Neural Networks: Theory and Applications (Adaptive and Learning Systems for Signal Processing, Communications and Control) New Hardcover
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$181.25 In Stock
Product details 272 pages Wiley-Interscience - English 9780471054368 Reviews:
"Synopsis" by , 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.
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