Inductive logic programming: Difference between revisions

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Structure Learning: rule sets don't predict actively
Structure Learning: rm leftover "more"
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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>
Its extension SLIPCOVER, proposed in 2014, uses bottom clauses generated as in [[Progol]] to guide the refinement process, thus reducing the number of revisions and exploring more the search space more effectively. Moreover, SLIPCOVER separates the search for promising clauses from that of the theory: the space of clauses is explored with a [[beam search]], while the space of theories is searched [[Greedy search|greedily]].<ref>{{Cite journal |last1=Bellodi |first1=Elena |last2=Riguzzi |first2=Fabrizio |date=2014-01-15 |title=Structure learning of probabilistic logic programs by searching the clause space |url=http://dx.doi.org/10.1017/s1471068413000689 |journal=Theory and Practice of Logic Programming |volume=15 |issue=2 |pages=169–212 |doi=10.1017/s1471068413000689 |arxiv=1309.2080 |s2cid=17669522 |issn=1471-0684}}</ref><ref name="pilp" />
 
==See also==