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Efficient and effective Bayesian network local structure learning
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DOI：10.1007/s11704-014-3335-z.png)
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摘要
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In this paper, we propose a more efficient Bayesian network structure learning algorithm under the framework of score based local learning (SLL). Our algorithm significantly improves computational efficiency by restricting the neighbors of each variable to a small subset of candidates and storing necessary information to uncover the spouses, at the same time guaranteeing to find the optimal neighbor set in the same sense as SLL. The algorithm is theoretically sound in the sense that it is optimal in the limit of large sample size. Empirical results testify its improved speed without loss of quality in the learned structures.
Keyword:
local structure learning
Bayesian network
Markov blanket
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4.6
论文数:
1.6K
被引数:
2.8K
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OAAI分享收藏The max-min hill-climbing Bayesian network structure learning algorithm最大最小爬山贝叶斯网络结构学习算法.png)
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