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dc.contributor.authorCicekli, I.en_US
dc.contributor.authorCicekli, N. K.en_US
dc.date.accessioned2018-04-12T13:44:43Z
dc.date.available2018-04-12T13:44:43Z
dc.date.issued2006-06en_US
dc.identifier.issn0924-669X
dc.identifier.urihttp://hdl.handle.net/11693/38112
dc.description.abstractThe least general generalization (LGG) of strings may cause an over-generalization in the generalization process of the clauses of predicates with string arguments. We propose a specific generalization (SG) for strings to reduce over-generalization. SGs of strings are used in the generalization of a set of strings representing the arguments of a set of positive examples of a predicate with string arguments. In order to create a SG of two strings, first, a unique match sequence between these strings is found. A unique match sequence of two strings consists of similarities and differences to represent similar parts and differing parts between those strings. The differences in the unique match sequence are replaced to create a SG of those strings. In the generalization process, a coverage algorithm based on SGs of strings or learning heuristics based on match sequences are used. © Springer Science + Business Media, LLC 2006.en_US
dc.language.isoEnglishen_US
dc.source.titleApplied Intelligence: the international journal of artificial intelligence, neural networks, and complex problem-solving technologiesen_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/s10489-006-8864-1en_US
dc.subjectInductive logic programmingen_US
dc.subjectMachine learningen_US
dc.subjectString generalizationen_US
dc.subjectAlgorithmsen_US
dc.subjectHeuristic methodsen_US
dc.subjectLearning systemsen_US
dc.subjectProcess controlen_US
dc.subjectArtificial intelligenceen_US
dc.titleGeneralizing predicates with string argumentsen_US
dc.typeArticleen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage23en_US
dc.citation.epage36en_US
dc.citation.volumeNumber25en_US
dc.identifier.doi10.1007/s10489-006-8864-1en_US
dc.publisherSpringer New York LLCen_US


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