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→Structure Learning: restored original phrasing, although it needs to be clarified |
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In the same year, Meert, W. et al. introduced a method for learning parameters and structure of [[Ground term|ground]] probabilistic logic programs by considering the [[Bayesian network]]s equivalent to them and applying techniques for learning Bayesian networks.<ref>{{Citation |last1=Blockeel |first1=Hendrik |title=Towards Learning Non-recursive LPADs by Transforming Them into Bayesian Networks |url=http://dx.doi.org/10.1007/978-3-540-73847-3_16 |work=Inductive Logic Programming |pages=94–108 |access-date=2023-12-09 |place=Berlin, Heidelberg |publisher=Springer Berlin Heidelberg |isbn=978-3-540-73846-6 |last2=Meert |first2=Wannes|series=Lecture Notes in Computer Science |date=2007 |volume=4455 |doi=10.1007/978-3-540-73847-3_16 }}</ref><ref name="pilp" />
ProbFOIL, introduced by De Raedt and Ingo Thon in 2010, combined the inductive logic programming system [[First-order inductive learner|FOIL]] with [[ProbLog]]. Logical rules are learned from probabilistic data in the sense that both the examples themselves and their classifications can be probabilistic. The set of rules
In 2011, Elena Bellodi and Fabrizio Riguzzi introduced SLIPCASE, which performs a beam search among probabilistic logic programs by iteratively refining probabilistic theories and optimizing the parameters of each theory using expectation-maximisation.<ref>{{Citation |last1=Bellodi |first1=Elena |title=Learning the Structure of Probabilistic Logic Programs |date=2012 |url=http://dx.doi.org/10.1007/978-3-642-31951-8_10 |work=Inductive Logic Programming |pages=61–75 |access-date=2023-12-09 |place=Berlin, Heidelberg |publisher=Springer Berlin Heidelberg |isbn=978-3-642-31950-1 |last2=Riguzzi |first2=Fabrizio|doi=10.1007/978-3-642-31951-8_10 }}</ref>
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