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Variable-Density Self-Organizing Map for Incremental Learning

Shimada, Atsushi ; Taniguchi, Rin-Ichiro

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We propose a new incremental learning method of Self-Organizing Map. Basically, there are three problems in the incremental learning of Self-Organizing Map: 1. depletion of neurons, 2. oblivion of training data previously given, 3. destruction of topological relationship among training samples. Weight-fixed neurons and weight-quasi-fixed neurons are very effective for the second problem. However the other problems still remain. Therefore, we improve the incremental learning method with weight-fixed neurons and weight-quasi-fixed neurons. We solve the problems by introducing a mechanism to increase the number of neurons effectively in the incremental learning process.

Keywords: Self-Organizing Map, Incremental Learning
Institution: Faculty of Technology, Research Groups in Informatics
DDC classification: Data processing, computer science, computer systems

Suggested Citation:
Shimada, Atsushi ; Taniguchi, Rin-Ichiro  (2007)  Variable-Density Self-Organizing Map for Incremental Learning.

URL: http://biecoll.ub.uni-bielefeld.de/volltexte/2007/174

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 Latest update: 15 Feb 2011
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