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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Published Online
2007-12-05
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(793KB)
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.
WANG Limin, LI Xiongfei, WANG Xuecheng.
Inference and learning in hybrid probabilistic network.
Front. Comput. Sci., 2007, 1(4): 429-435 DOI:10.1007/s11704-007-0041-0
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