Robust estimation for mixture of probability tables based on β-likelihood

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

Modeling of a large joint probability table is problematic when its variables have a large number of categories. In such a case, a mixture of simpler probability tables could be a good model. And the estimation of such a large probability table frequently has another problem of data sparseness. When constructing mixture models with sparse data, EM estimators based on the β-likelihood are expected more appropriate than those based on the log likelihood. Experimental results show that a mixture model estimated by the βlikelihood approximates a large joint probability table with sparse data more appropriately than EM estimators.

本文言語English
ホスト出版物のタイトルProceedings of the Sixth SIAM International Conference on Data Mining
出版社Society for Industrial and Applied Mathematics
ページ519-523
ページ数5
ISBN(印刷版)089871611X, 9780898716115
DOI
出版ステータスPublished - 2006
イベントSixth SIAM International Conference on Data Mining - Bethesda, MD, United States
継続期間: 2006 4 202006 4 22

出版物シリーズ

名前Proceedings of the Sixth SIAM International Conference on Data Mining
2006

Conference

ConferenceSixth SIAM International Conference on Data Mining
CountryUnited States
CityBethesda, MD
Period06/4/2006/4/22

ASJC Scopus subject areas

  • Engineering(all)

引用スタイル