Semi-supervised learning scheme using Dirichlet process EM-algorithm (パターン認識・メディア理解)
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概要
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Learning with a dataset that contains both labeled data and unlabeled data is often called a semi-supervised learning problem. In the last decade, the semi-supervised learning problem has become an important research problem in many fields. This article presents a novel semi-supervised learning scheme using a Bayesian Maximum A Posteriori Expectation Maximization (MAP-EM) algorithm with a Dirichlet process prior (stick-breaking representation). The proposed scheme enables us to estimate a mixture model under an unknown number of components and provides a simpler implementation than other implementations such as Markov Chain Monte Carlo (MCMC) implementations. Several examples of a Gaussian mixture are examined to validate the proposed scheme.
- 2009-03-06
著者
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Matsumoto Takashi
Faculty Of Engineering Department Of Electronic Engineering Yamanashi University
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Doucet Arnaud
The Institute Of Statistical Mathematics
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Kimura Tomoaki
Graduate School Of Advanced Science And Engineering Waseda University
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NAKADA Yohei
Faculty of Science and Engineering, Waseda University
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Nakada Yohei
Faculty Of Science And Engineering Waseda University
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Matsumoto Takashi
Faculty Of Science And Engineering Waseda University
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Matsumoto Takashi
Faculty Of Agriculture Shinshu University
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