Self-Organizing Maps

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Springer Science & Business Media, Dec 6, 2012 - Computers - 426 pages
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The second, revised edition of this book was suggested by the impressive sales of the first edition. Fortunately this enabled us to incorporate new important results that had just been obtained. The ASSOM (Adaptive-Subspace SOM) is a new architecture in which invariant-feature detectors emerge in an unsupervised learning process. Its basic principle was already introduced in the first edition, but the motiva tion and theoretical discussion in the second edition is more thorough and consequent. New material has been added to Sect. 5.9 and this section has been rewritten totally. Correspondingly, Sect. 1.4, which deals with adaptive subspace classifiers in general and constitutes the prerequisite for the ASSOM principle, has also been extended and rewritten totally. Another new SOM development is the WEBSOM, a two-layer architecture intended for the organization of very large collections of full-text documents such as those found in the Internet and World Wide Web. This architecture was published after the first edition came out. The idea and results seemed to be so important that the new Sect. 7.8 has now been added to the second edition. Another addition that contains new results is Sect. 3.15, which describes the acceleration in the computing of very large SOMs. It was also felt that Chap. 7, which deals with 80M applications, had to be extended.
 

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Contents

Mathematical Preliminaries
1
Justification of Neural Modeling
59
The Basic SOM 85
84
in the Output Plane
96
to SelfOrganization
103
Physiological Interpretation of SOM
145
Variants of SOM
157
by Stochastic Approximation
186
Learning Vector Quantization 203
202
Applications
219
Hardware for SOM
261
An Overview of SOM Literature
277
Glossary of Neural Terms 303
302
References
333
Index
413
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