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.
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Wang, L., Wang, X. & Li, X. Inference and learning in hybrid probabilistic network. Front. Comput. Sc. China 1, 429–435 (2007). https://doi.org/10.1007/s11704-007-0041-0
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DOI: https://doi.org/10.1007/s11704-007-0041-0