An automatic rule creating method for Kansei data and its application to a font creating system

Hajime Hotta, Masafumi Hagiwara

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

1 Citation (Scopus)

Abstract

In this paper, we propose a method for creating fuzzy rules of Kansei data automatically. This method consists of 3 steps: (1) Generation of pseudo data of Kansei data by a General Regression Neural Network; (2) Clustering the pseudo data by a Fuzzy ART; (3) Translating each cluster into a fuzzy rule and extracting important rules. In this experiment, we applied this method to "a Japanese font creating system reflecting user's Kansei ." From the result of the experiment, although we have used the same algorithm for drawing font outlines, the system employing our method can reflect Kansei better than the conventional one.

Original languageEnglish
Title of host publicationModeling Decisions for Artificial Intelligence - Second International Conference, MDAI 2005, Proceedings
PublisherSpringer Verlag
Pages421-430
Number of pages10
ISBN (Print)3540278710, 9783540278719
DOIs
Publication statusPublished - 2005
Event2nd International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2005 - Tsukuba, Japan
Duration: 2005 Jul 252005 Jul 27

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3558 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2nd International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2005
CountryJapan
CityTsukuba
Period05/7/2505/7/27

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Hotta, H., & Hagiwara, M. (2005). An automatic rule creating method for Kansei data and its application to a font creating system. In Modeling Decisions for Artificial Intelligence - Second International Conference, MDAI 2005, Proceedings (pp. 421-430). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3558 LNAI). Springer Verlag. https://doi.org/10.1007/11526018_41