Advances in Independent Component Analysis (Paperback, 2000 ed.)

Independent Component Analysis (ICA) is a fast developing area of intense research interest. Following on from Self-Organising Neural Networks: Independent Component Analysis and Blind Signal Separation, this book reviews the significant developments of the past year.It covers topics such as the use of hidden Markov methods, the independence assumption, and topographic ICA, and includes tutorial chapters on Bayesian and variational approaches. It also provides the latest approaches to ICA problems, including an investigation into certain "hard problems" for the very first time.Comprising contributions from the most respected and innovative researchers in the field, this volume will be of interest to students and researchers in computer science and electrical engineering; research and development personnel in disciplines such as statistical modelling and data analysis; bio-informatic workers; and physicists and chemists requiring novel data analysis methods.

General

Imprint:	Springer London
Country of origin:	United Kingdom
Series:	Perspectives in Neural Computing
Release date:	2001
First published:	2000
Editors:	Mark Girolami
Dimensions:	235 x 155 x 16mm (L x W x T)
Format:	Paperback
Pages:	284
Edition:	2000 ed.
ISBN-13:	978-1-85233-263-1
Categories:	Books > Computing & IT > Applications of computing > Artificial intelligence > Neural networks
LSN:	1-85233-263-8
Barcode:	9781852332631

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