Semi-Supervised Learning by Gaussian Mixtures

被引：0

作者：

Choi, Byoung-Jeong ^{[1
]}

Chae, Youn-Seok ^{[2
]}

Choi, Woo-Young ^{[2
]}

Park, Changyi ^{[3
]}

Koo, Ja-Yong ^{[4
]}

机构：

[1] Korea Univ, Dept Stat, SAS Korea, 9F,Daechi B-D,889-11,Daechi Dong, Seoul 135839, South Korea

[2] SAS Korea, Seoul 135839, South Korea

[3] Univ Seoul, Dept Stat, Seoul 130743, South Korea

[4] Korea Univ, Dept Stat, Seoul 136701, South Korea

来源：

KOREAN JOURNAL OF APPLIED STATISTICS | 2008年 / 21卷 / 05期

关键词：

BIC; classification; density estimation; EM algorithm; Gaussian mixture;

D O I：

暂无

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Discriminant analysis based on Gaussian mixture models, an useful tool for multi-class classifications, can be extended to semi-supervised learning. We consider a model selection problem for a Gaussian mixture model in semi-supervised learning. More specifically, we adopt Bayesian information criterion to determine the number of subclasses in the mixture model. Through simulations, we illustrate the usefulness of the criterion.

引用

页码：825 / 833

页数：9

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