| Description |
xviii, 383 pages : illustrations ; 24 cm |
| Series |
Advanced information and knowledge processing |
|
Advanced information and knowledge processing.
|
| Contents |
1. Introduction -- 2. Background -- 3. The negative feedback network -- 4. Peer-inhibitory neurons -- 5. Multiple cause data -- 6. Exploratory data analysis -- 7. Topology preserving maps -- 8. Maximum likelihood Hebbian learning -- 9. Two neural networks for canonical correlation analysis -- 10. Alternative derivations of CCA networks -- 11. Kernel and nonlinear correlations -- 12. Exploratory correlation analysis -- 13. Multicollinearity and partial least squares -- 14. Twinned principal curves -- 15. The future -- A. Negative feedback artificial neural networks -- B. Previous factor analysis models -- C. Related models for ICA -- D. Previous dual stream approaches -- E. Data sets |
| Summary |
"The central idea of Hebbian Learning and Negative Feedback Networks is that artificial neural networks using negative feedback of activation can use simple Hebbian learning to self-organise in such a way that they uncover interesting structures in data sets." "The book encompasses a wide range of real experiments and displays how the approaches it formulates can be applied to the analysis of real problems. It will be of particular interest to postgraduates and academic or industrial researchers in the neuroscience community."--BOOK JACKET |
| Notes |
Formerly CIP. Uk |
| Bibliography |
Includes bibliographical references (pages [371]-379) and index |
| Subject |
Data mining.
|
|
Neural networks (Computer science)
|
| LC no. |
2004052217 |
| ISBN |
1852338830 alkaline paper |
|
