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Springer Advances in Independent Component Analysis - Springer

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Description

Advances in Independent Component Analysis examines the latest developments in this fast-growing research field, building on foundational work in self-organising neural networks and blind signal separation. This Springer publication explores hidden Markov methods, independence assumptions, and topographic ICA techniques alongside tutorial chapters on Bayesian and variational approaches. The book addresses emerging solutions to ICA problems and investigates challenging topics in the discipline for the first time, making it essential reading for researchers, practitioners, and students working in artificial intelligence, machine learning, and signal processing who need to understand cutting-edge independent component analysis methodologies and their practical applications.

Key Features

Covers hidden Markov methods and independence assumptions within independent component analysis frameworks.

Includes tutorial chapters on Bayesian and variational approaches to ICA problem-solving.

Addresses hard problems in independent component analysis with novel investigative approaches.

Explores topographic ICA techniques and their applications in neural computing.

Published by Springer as part of the Perspectives in Neural Computing series for advanced study.

Product Specifications

Format
paperback
Domain
Amazon UK
Publication Date
17 July 2000
Listed Since
15 January 2007

Barcode

No barcode data available

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