Enabling Large-Scale Bayesian Network Learning by Preserving Intercluster Directionality(Artificial Intelligence and Cognitive Science)
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概要
- 論文の詳細を見る
We propose a recursive clustering and order restriction (R-CORE) method for learning large-scale Bayesian networks. The proposed method considers a reduced search space for directed acyclic graph (DAG) structures in scoring-based Bayesian network learning. The candidate DAG structures are restricted by clustering variables and determining the intercluster directionality. The proposed method considers cycles on only c_<max>(<<n) variables rather than on all n variables for DAG structures. The R-CORE method could be a useful tool in very large problems where only a very small amount of training data is available.
- 社団法人電子情報通信学会の論文
- 2007-07-01
著者
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LEE Doheon
Department of BioSystems and Advanced Information Technology Research Center
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Lee Kwang
Department Of Bioengineering Yonsei University
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Lee Doheon
Department Of Biosystems Kaist
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JUNG Sungwon
Department of Electrical Engineering & Computer Science, KAIST
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Jung Sungwon
Department Of Computer Science Sogang University
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Jung Sungwon
Department Of Electrical Engineering & Computer Science Kaist
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Lee Kwang
Department Of Biosystems Aitrc Kaist
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Lee Kwang
Department of Advanced Technology Fusion (DATF), Konkuk University, 1 Hwayang-dong, Gwangjin-gu, Seoul 143-701, Korea
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