Variable-density Self-Organizing Map for incremental learning

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationWSOM 2007 - 6th Int. Workshop on Self-Organizing Maps
Publication statusPublished - 2007
Event6th Int. Workshop on Self-Organizing Maps, WSOM 2007 - Bielefeld, Germany
Duration: Sep 3 2007Sep 6 2007

Other

Other6th Int. Workshop on Self-Organizing Maps, WSOM 2007
CountryGermany
CityBielefeld
Period9/3/079/6/07

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All Science Journal Classification (ASJC) codes

  • Software

Cite this

Shimada, A., & Taniguchi, R-I. (2007). Variable-density Self-Organizing Map for incremental learning. In WSOM 2007 - 6th Int. Workshop on Self-Organizing Maps