Self-Organising Neural Networks : Independent Somponent Analysis and Blind Source Separation : Perspectives in Neural Computing - Mark Girolami

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Self-Organising Neural Networks : Independent Somponent Analysis and Blind Source Separation

By: Mark Girolami

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284 Pages

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23.5 x 16.51 x 1.27

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The conception of fresh ideas and the development of new techniques for Blind Source Separation and Independent Component Analysis have been rapid in recent years. It is also encouraging, from the perspective of the many scientists involved in this fascinating area of research, to witness the growing list of successful applications of these methods to a diverse range of practical everyday problems. This growth has been due, in part, to the number of promising young and enthusiastic researchers who have committed their efforts to expanding the current body of knowledge within this field of research. The author of this book is among one of their number. I trust that the present book by Dr. Mark Girolami will provide a rapid and effective means of communicating some of these new ideas to a wide international audience and that in turn this will expand further the growth of knowledge. In my opinion this book makes an important contribution to the theory of Independent Component Analysis and Blind Source Separation. This opens a range of exciting methods, techniques and algorithms for applied researchers and practitioner engineers, especially from the perspective of artificial neural networks and information theory. It has been interesting to see how rapidly the scientific literature in this area has grown.

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Science Artificial Intelligence Neural Networks & Fuzzy Systems Computer Vision

Foreword
Introduction	p. 1
Background to Blind Source Separation	p. 5
Fourth Order Cumulant Based Blind Source Separation	p. 35
Self-Organising Neural Networks	p. 47
The Non-Linear PCA Algorithm and Blind Source Separation	p. 77
Non-Linear Feature Extraction and Blind Source Separation	p. 119
Information Theoretic Non-Linear Feature Extraction And Blind Source Separation	p. 165
Temporal Anti-Hebbian Learning	p. 201
Applications	p. 239
References	p. 255
Index	p. 269
Table of Contents provided by Blackwell. All Rights Reserved.

Self-Organising Neural Networks : Independent Somponent Analysis and Blind Source Separation

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