Principal Component Neural Networks: Theory and Applications Book

Principal Component Neural Networks: Theory and Applications, 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. Usi, Principal Component Neural Networks: Theory and Applications

5 out of 5 stars based on 2 reviews

Principal Component Neural Networks: Theory and Applications
Written by author Kostas I. Diamantaras
Published by Wiley, John & Sons, Incorporated, March 1996
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. Usi
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. Usi

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Book Categories

Authors

Preface
1 Introduction 1
2 A Review of Linear Algebra 14
3 Principal Component Analysis 44
4 PCA Neural Networks 74
5 Channel Noise and Hidden Units 122
6 Heteroassociative Models 146
7 Signal Enhancement Against Noise 182
8 VLSI Implementation 205
Appendix A Stochastic Approximation 229
Appendix B Derivatives with Vectors and Matrices 235
Appendix C Compactness and Convexity 237
Bibliography 241
Index 249

Title: Principal Component Neural Networks: Theory and Applications

Manufacturer:

Wiley

Item Number: 9780471054368

Publication Date: March 1996

Number: 1

Product Description: Full Name: Principal Component Neural Networks: Theory and Applications; Short Name:Principal Component Neural Networks

Universal Product Code (UPC): 9780471054368

WonderClub Stock Keeping Unit (WSKU): 9780471054368

Rating: 5/5 based on 2 Reviews

Image Location: https://wonderclub.com/images/covers/43/68/9780471054368.jpg

Category: Media >> Books

Weight: 0.200 kg (0.44 lbs)

Width: 6.340 cm (2.50 inches)

Heigh : 9.490 cm (3.74 inches)

Depth: 0.790 cm (0.31 inches)

Date Added: August 25, 2020, Added By: Ross

Date Last Edited: August 25, 2020, Edited By: Ross

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$135.98	Digital	Arrives between May 11 - Jun 10 Login to your account for a direct download. Some files are emailed from Dropbox. Continent $0.00 - World $0.00	WonderClub (9296 total ratings)

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Scott Silva

reviewed Principal Component Neural Networks: Theory and Applications on December 01, 2019

Principal Component Neural Networks: Theory and Applications

Fascinating read but quite outdated

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Principal Component Neural Networks: Theory and Applications, 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. Usi, Principal Component Neural Networks: Theory and Applications

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Principal Component Neural Networks: Theory and Applications Book

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	Preface
1	Introduction	1
2	A Review of Linear Algebra	14
3	Principal Component Analysis	44
4	PCA Neural Networks	74
5	Channel Noise and Hidden Units	122
6	Heteroassociative Models	146
7	Signal Enhancement Against Noise	182
8	VLSI Implementation	205
	Appendix A Stochastic Approximation	229
	Appendix B Derivatives with Vectors and Matrices	235
	Appendix C Compactness and Convexity	237
	Bibliography	241
	Index	249