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	Hybrid incremental learning algorithms for Bayesian network structures
	Shi, Da ; Tan, Shaohua
	2010
英文摘要	Hybrid learning can reduce the computational complexity of incremental algorithms for Bayesian network structures significantly. In this paper, a group of hybrid incremental algorithms are proposed. The central idea of these algorithms is to use the polynomial-time constraint-based technique to build a candidate parent set for each domain variable, followed by the hill climbing search procedure to refine the current network structure under the guidance of the candidate parent sets. The experimental results show that, our hybrid incremental algorithms offer considerable computational complexity savings while obtaining better model accuracy compared to the existing incremental algorithms. ? 2010 IEEE.; EI; 0
语种	英语
DOI标识	10.1109/COGINF.2010.5599716
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/295406]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Shi, Da,Tan, Shaohua. Hybrid incremental learning algorithms for Bayesian network structures. 2010-01-01.

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