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Self-Organizing Maps

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The Self-Organizing Map (SOM), with its variants, is the most popular artificial neural network algorithm in the unsupervised learning category. Many fields of science have adopted the SOM as a standard analytical tool: in statistics,signal processing, control theory, financial analyses, experimental physics, chemistry and medicine. A new area is organization of very large document collections. The SOM is also one of the most realistic models of the biological brain functions.This new edition includes a survey of over 2000 contemporary studies to cover the newest results; the case examples were provided with detailed formulae, illustrations and tables; a new chapter on software tools for SOM was written, other chapters were extended or reorganized.

Author(s): Teuvo Kohonen
Series: Springer Series in Information Sciences 30
Edition: 3
Publisher: Springer
Year: 1997

Language: English
Pages: 260
City: Berlin; New York
Tags: Biophysics and Biological Physics;Communications Engineering, Networks;Mathematics, general

Front Matter....Pages I-XVII
Mathematical Preliminaries....Pages 1-58
Justification of Neural Modeling....Pages 59-83
The Basic SOM....Pages 85-144
Physiological Interpretation of SOM....Pages 145-155
Variants of SOM....Pages 157-201
Learning Vector Quantization....Pages 203-217
Applications....Pages 219-260
Hardware for SOM....Pages 261-276
An Overview of SOM Literature....Pages 277-301
Glossary of “Neural” Terms....Pages 303-331
Back Matter....Pages 333-428