Self-Organising Neural Networks: Independent Component Analysis and Blind Source Separation by Mark GirolamiSelf-Organising Neural Networks: Independent Component Analysis and Blind Source Separation by Mark Girolami

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

byMark Girolami

Paperback

Pricing and Purchase Info

$209.54 online 
$219.50 list price
Earn 1,048 plum® points

Prices and offers may vary in store

Quantity:

In stock online

Ships free on orders over $25

Not available in stores

about

This volume presents the theory and applications of self-organising neural network models which perform the Independent Component Analysis (ICA) transformation and Blind Source Separation (BSS). It is largely self-contained, covering the fundamental concepts of information theory, higher order statistics and information 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 visualisation, data mining and biomedical data analysis. Self-Organising Neural Networks will be of interest to postgraduate students and researchers in Connectionist AI, Signal Processing and Neural Networks, research and development workers, and technology development engineers and research engineers.
Title:Self-Organising Neural Networks: Independent Component Analysis and Blind Source SeparationFormat:PaperbackDimensions:271 pages, 23.5 × 15.5 × 0.01 inPublisher:Springer-Verlag/Sci-Tech/Trade

The following ISBNs are associated with this title:

ISBN - 10:185233066X

ISBN - 13:9781852330668

Look for similar items by category:

Reviews

Table of Contents

Introduction.- Background to Blind Source Separation (BSS).- Fourth Order Cumulant Based BSS.- Self Organising Neural Networks.- The Non-Linear PCA Algorithm and BSS.- Nonlinear Feature Extraction and BSS.- Information Theoretic Nonlinear Feature Extraction and BSS.- Temporal Anti-Hebbian Learning and Blind Separation of Convolutive Mixtures of Sources.- Conclusions.- Bibliography.