Learning a generative failure-free PRISM clause
نویسندگان
چکیده
PRISM is a probabilistic logic programming formalism which allows learning parameters from examples through its graphical EM algorithm. PRISM is aimed at modelling generative processes in the compact first-order logic representation. It facilitates model selection by providing three scoring functions Bayesian Information Criterion (BIC), Cheeseman-Stutz (CS) and Variational free energy. This paper considers learning failure-free single clause PRISM program by searching and scoring possible models built from observations and Background Knowledge (BK).
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