| Advances in Independent Component Analysis 2000 Edition Contributor(s): Girolami, Mark (Editor) |
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ISBN: 1852332638 ISBN-13: 9781852332631 Publisher: Springer OUR PRICE: $189.99 Product Type: Paperback Published: July 2000 Annotation: 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. |
| Additional Information |
| BISAC Categories: - Gardening - Computers | Networking - General - Computers | Intelligence (ai) & Semantics |
| Dewey: 006.32 |
| LCCN: 00037370 |
| Series: Perspectives in Neural Computing |
| Physical Information: 0.7" H x 6.46" W x 9.12" (1.00 lbs) 284 pages |
| Descriptions, Reviews, Etc. |
| Publisher 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. |