Self-Organising Neural Networks - Independent Component Analysis and Blind Source Separation (Paperback, Edition. ed.)

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.

General

Imprint:	Springer London
Country of origin:	United Kingdom
Series:	Perspectives in Neural Computing
Release date:	June 1999
First published:	1999
Authors:	Mark Girolami
Dimensions:	235 x 155 x 15mm (L x W x T)
Format:	Paperback
Pages:	271
Edition:	Edition. ed.
ISBN-13:	978-1-85233-066-8
Categories:	Books > Computing & IT > Applications of computing > Artificial intelligence > Neural networks
LSN:	1-85233-066-X
Barcode:	9781852330668

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