Self-growing learning vector quantization with additional learning and rule extraction abilities

Dan Mikami, Masafumi Hagiwara

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

In this paper, we propose a self-growing learning vector quantization (SGLVQ). The proposed SGLVQ is constructed based on the self-organizing map (SOM) and the learning vector quantization (LVQ). Learning of the SGLVQ consists of 3 steps: SOM step, LVQ step, and rule extraction step. In the LVQ step, neurons are incremented and the size of the network is adjusted automatically. The incrementation of neurons enables additional learning and contributes to obtain high recognition ability. In the rule extraction step, rules can be extracted. Computer experiments show the improvement of the recognition rate, the ability of additional learning and extraction of the rules.

Original languageEnglish
Pages (from-to)2895-2900
Number of pages6
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume4
DOIs
Publication statusPublished - 2000 Jan 1

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Hardware and Architecture

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