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dc.contributor.authorOmran, PG
dc.contributor.authorWang, K
dc.contributor.authorWang, Z
dc.date.accessioned2018-09-11T05:01:30Z
dc.date.available2018-09-11T05:01:30Z
dc.date.issued2016
dc.identifier.issn0302-9743
dc.identifier.doi10.1007/978-3-319-50127-7_33
dc.identifier.urihttp://hdl.handle.net/10072/124136
dc.description.abstractSeveral approaches to learning probabilistic logic programs have been proposed in the literature. However, most learning systems based on these approaches are not efficient for handling large practical problems (especially, in the case of structure learning). It has been a challenging issue to reduce the search space of candidate (probabilistic) logic programs. There is no exception for SLIPCOVER, a latest system for both parameter and structure learning of Logic Programs with Annotated Disjunction (LPADs). This paper presents a new algorithm T-LPAD for structure learning of LPADs by employing transfer learning. The new algorithm has been implemented and our experimental results show that T-LPAD outperforms SLIPCOVER (and SLIPCASE) for most benchmarks used in related systems.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofpagefrom378
dc.relation.ispartofpageto389
dc.relation.ispartofjournalLecture Notes in Computer Science
dc.relation.ispartofvolume9992
dc.subject.fieldofresearchArtificial intelligence
dc.subject.fieldofresearchInformation and computing sciences
dc.subject.fieldofresearchcode4602
dc.subject.fieldofresearchcode46
dc.titleTransfer Learning in Probabilistic Logic Models
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.hasfulltextNo Full Text
gro.griffith.authorWang, Kewen
gro.griffith.authorWang, Zhe


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