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

Dan Mikami, Masafumi Hagiwara

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

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
Title of host publicationProceedings of the IEEE International Conference on Systems, Man and Cybernetics
PublisherIEEE
Pages2895-2900
Number of pages6
Volume4
Publication statusPublished - 2000
Event2000 IEEE International Conference on Systems, Man and Cybernetics - Nashville, TN, USA
Duration: 2000 Oct 82000 Oct 11

Other

Other2000 IEEE International Conference on Systems, Man and Cybernetics
CityNashville, TN, USA
Period00/10/800/10/11

Fingerprint

Vector quantization
Self organizing maps
Neurons
Experiments

ASJC Scopus subject areas

  • Hardware and Architecture
  • Control and Systems Engineering

Cite this

Mikami, D., & Hagiwara, M. (2000). Self-growing learning vector quantization with additional learning and rule extraction abilities. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (Vol. 4, pp. 2895-2900). IEEE.

Self-growing learning vector quantization with additional learning and rule extraction abilities. / Mikami, Dan; Hagiwara, Masafumi.

Proceedings of the IEEE International Conference on Systems, Man and Cybernetics. Vol. 4 IEEE, 2000. p. 2895-2900.

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

Mikami, D & Hagiwara, M 2000, Self-growing learning vector quantization with additional learning and rule extraction abilities. in Proceedings of the IEEE International Conference on Systems, Man and Cybernetics. vol. 4, IEEE, pp. 2895-2900, 2000 IEEE International Conference on Systems, Man and Cybernetics, Nashville, TN, USA, 00/10/8.
Mikami D, Hagiwara M. Self-growing learning vector quantization with additional learning and rule extraction abilities. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics. Vol. 4. IEEE. 2000. p. 2895-2900
Mikami, Dan ; Hagiwara, Masafumi. / Self-growing learning vector quantization with additional learning and rule extraction abilities. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics. Vol. 4 IEEE, 2000. pp. 2895-2900
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