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dc.contributor.authorSteffen Hölldobler
dc.contributor.authorCarsten Lutz
dc.contributor.authorHeinrich Wansing
dc.contributor.authorJ.G. Carbonell
dc.contributor.authorJ. Siekmann
dc.date.accessioned2018-05-21T07:33:39Z
dc.date.available2018-05-21T07:33:39Z
dc.date.issued2006
dc.identifier.citationhttps://link-springer-com.adu-lib-database.idm.oclc.org/book/10.1007/978-3-540-87803-2en_US
dc.identifier.isbn978-3-540-39625-3
dc.identifier.urihttps://dspace.adu.ac.ae/handle/1/1351
dc.descriptionLutz, S. H. C., & Wansing, H. (2008). Logics in Artificial Intelligence.en_US
dc.description.abstractSituated at the intersection of machine learning and logic programming, inductive logic programming (ILP) has been concerned with finding patterns expressed as logic programs. While ILP initially focussed on automated program synthesis from examples, it has recently expanded its scope to cover a whole range of data analysis tasks (classification, regression, clustering, association analysis). ILP algorithms can this be used to find patterns in relational data, i.e., for relational data mining (RDM). This paper briefly introduces the basic concepts of ILP and RDM and discusses some recent research trends in these areas.en_US
dc.language.isoen_USen_US
dc.publisherSpringeren_US
dc.subjectArtificial Intelligenceen_US
dc.subjectLogic Programmingen_US
dc.subjectData Analysisen_US
dc.subjectRelational Data Miningen_US
dc.titleLogics in Artificial Intelligenceen_US
dc.typeBooken_US
dc.identifier.doihttps://doi.org/10.1007/978-3-540-87803-2


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