The Korean Journal of Applied Statistics (응용통계연구)
- Volume 21 Issue 5
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- Pages.825-833
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- 2008
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- 1225-066X(pISSN)
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- 2383-5818(eISSN)
DOI QR Code
Semi-Supervised Learning by Gaussian Mixtures
정규 혼합분포를 이용한 준지도 학습
- Choi, Byoung-Jeong (Dept. of Statistics, Korea University) ;
- Chae, Youn-Seok (Consultant, SAS Korea) ;
- Choi, Woo-Young (Consultant, SAS Korea) ;
- Park, Chang-Yi (Dept. of Statistics, University of Seoul) ;
- Koo, Ja-Yong (Dept. of Statistics, Korea University)
- 최병정 (고려대학교 통계학과) ;
- 채윤석 (SAS Korea, 컨설턴트) ;
- 최우영 (SAS Korea, 컨설턴트) ;
- 박창이 (서울시립대학교 통계학과) ;
- 구자용 (고려대학교 통계학과)
- Published : 2008.10.31
Abstract
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.
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References
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