Date: 14 Mar 2003

Revision of First-Order Bayesian Classifiers

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Abstract

New representation languages that integrate first order logic with Bayesian networks have been proposed in the literature. Probabilistic Relational models (PRM) and Bayesian Logic Programs (BLP) are examples. Algorithms to learn both the qualitative and the quantitative components of these languages have been developed. Recently, we have developed an algorithm to revise a BLP. In this paper, we discuss the relationship among these approaches, extend our revision algorithm to return the highest probabilistic scoring BLP and argue that for a classi- fication task our approach, which uses techniques of theory revision and so searches a smaller hypotheses space, can be a more adequate choice.