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

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

Perspectives in Neural Computing

Paperback ISBN: 9781852330668
Number Of Pages: 271

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This volume presents the theory and applications of self organizing neural network models which perform the independent component analysis (ICA) transformation and blind source separation (BSS). It covers the fundamental concepts of information theory, higher order statistics and in formation geometry. Neural models for instantaneous and temporal BSS and their adaptation algorithms are presented and studied in detail. There is also in-depth coverage of the following application areas: noise reduction, speech enhancement in noisy environments, image enhancement, feature extraction for classification, data analysis and visualization, data mining and biomedical data analysis.

Foreword
Introductionp. 1
Background to Blind Source Separationp. 5
Fourth Order Cumulant Based Blind Source Separationp. 35
Self-Organising Neural Networksp. 47
The Non-Linear PCA Algorithm and Blind Source Separationp. 77
Non-Linear Feature Extraction and Blind Source Separationp. 119
Information Theoretic Non-Linear Feature Extraction And Blind Source Separationp. 165
Temporal Anti-Hebbian Learningp. 201
Applicationsp. 239
Referencesp. 255
Indexp. 269
Table of Contents provided by Blackwell. All Rights Reserved.

ISBN: 9781852330668
ISBN-10: 185233066X
Series: Perspectives in Neural Computing
Audience: General
Format: Paperback
Language: English
Number Of Pages: 271
Publisher: Springer London Ltd
Country of Publication: GB
Dimensions (cm): 23.5 x 15.85  x 2.08
Weight (kg): 0.45

Earn 317 Qantas Points
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