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Semi-Supervised Learning by Gaussian Mixtures

정규 혼합분포를 이용한 준지도 학습

  • 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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