A generalization of independence in naive bayes model

研究成果: Conference contribution

8 被引用数 (Scopus)

抄録

In this paper, generalized statistical independence is proposed from the viewpoint of generalized multiplication characterized by a monotonically increasing function and its inverse function, and it is implemented in naive Bayes models. This paper also proposes an idea of their estimation method which directly uses empirical marginal distributions to retain simplicity of calculation. Our method is interpreted as an optimization of a rough approximation of the Bregman divergence so that it is expected to have a kind of robust property. Effectiveness of our proposed models is shown by numerical experiments on some benchmark data sets.

本文言語English
ホスト出版物のタイトルIntelligent Data Engineering and Automated Learning, IDEAL 2010 - 11th International Conference, Proceedings
ページ153-161
ページ数9
DOI
出版ステータスPublished - 2010 11 8
イベント11th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2010 - Paisley, United Kingdom
継続期間: 2010 9 12010 9 3

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
6283 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference11th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2010
CountryUnited Kingdom
CityPaisley
Period10/9/110/9/3

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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