### 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 |
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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 20 → 2006 4 22 |

### 出版物シリーズ

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

### Conference

Conference | Sixth SIAM International Conference on Data Mining |
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国 | United States |

市 | Bethesda, MD |

期間 | 06/4/20 → 06/4/22 |

### 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).