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Advances in Independent Component Analysis

Livre broché | Anglais | Perspectives in Neural Computing
305,45 €
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Description

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.

Spécifications

Parties prenantes

Editeur:

Contenu

Nombre de pages :
284
Langue:
Anglais
Collection :

Caractéristiques

EAN:
9781852332631
Date de parution :
17-07-00
Format:
Livre broché
Format numérique:
Trade paperback (VS)
Dimensions :
164 mm x 232 mm
Poids :
453 g

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