Overview
- A state-of-the-art overview with contributions from the most respected and innovative researchers in the field
- Contains significantly more advanced, novel and up-to-date theory than any other volume available
Part of the book series: Perspectives in Neural Computing (PERSPECT.NEURAL)
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Table of contents (14 chapters)
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Front Matter
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Temporal ICA Models
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Front Matter
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The Validity of the Independence Assumption
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Front Matter
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Ensemble Learning and Applications
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Front Matter
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Data Analysis and Applications
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Front Matter
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Back Matter
Keywords
About this book
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.
Editors and Affiliations
Bibliographic Information
Book Title: Advances in Independent Component Analysis
Editors: Mark Girolami
Series Title: Perspectives in Neural Computing
DOI: https://doi.org/10.1007/978-1-4471-0443-8
Publisher: Springer London
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eBook Packages: Springer Book Archive
Copyright Information: Springer-Verlag London 2000
Softcover ISBN: 978-1-85233-263-1Published: 17 July 2000
eBook ISBN: 978-1-4471-0443-8Published: 06 December 2012
Series ISSN: 1431-6854
Edition Number: 1
Number of Pages: XX, 284
Number of Illustrations: 19 b/w illustrations
Topics: Artificial Intelligence, Computer Appl. in Life Sciences, Computation by Abstract Devices