Inference and learning in hybrid probabilistic network

doi:10.1007/s11704-007-0041-0

Front. Comput. Sci.

2007, Vol. 1

Issue (4) : 429-435 https://doi.org/10.1007/s11704-007-0041-0

Inference and learning in hybrid probabilistic network

WANG Limin¹, LI Xiongfei¹, WANG Xuecheng²

1.Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China; 2.The Institute of Information Spreading Engineering, Changchun University of Technology, Changchun 130012, China;

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Abstract This paper proposed a novel hybrid probabilistic network, which is a good tradeoff between the model complexity and learnability in practice. It relaxes the conditional independence assumptions of Naive Bayes while still permitting efficient inference and learning. Experimental studies on a set of natural domains prove its clear advantages with respect to the generalization ability.

Issue Date: 05 December 2007

Cite this article:

WANG Xuecheng,WANG Limin,LI Xiongfei. Inference and learning in hybrid probabilistic network[J]. Front. Comput. Sci., 2007, 1(4): 429-435.

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https://academic.hep.com.cn/fcs/EN/10.1007/s11704-007-0041-0
https://academic.hep.com.cn/fcs/EN/Y2007/V1/I4/429

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