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

研究成果: Conference contribution

2 引用 (Scopus)

抜粋

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.

元の言語English
ホスト出版物のタイトルProceedings of the Sixth SIAM International Conference on Data Mining
ページ519-523
ページ数5
出版物ステータスPublished - 2006 7 3
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
United States
Bethesda, MD
期間06/4/2006/4/22

ASJC Scopus subject areas

  • Engineering(all)

これを引用

Fujimoto, Y., & Murata, N. (2006). Robust estimation for mixture of probability tables based on β-likelihood. : Proceedings of the Sixth SIAM International Conference on Data Mining (pp. 519-523). (Proceedings of the Sixth SIAM International Conference on Data Mining; 巻数 2006).