Grammatical inference of directed acyclic graph languages with polynomial time complexity

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dc.contributorReconocimiento de Formas e Inteligencia Artificiales_ES
dc.contributor.authorGallego, Antonio-Javier-
dc.contributor.authorLópez Rodríguez, Damián-
dc.contributor.authorCalera Rubio, Jorge-
dc.contributor.otherUniversidad de Alicante. Departamento de Lenguajes y Sistemas Informáticoses_ES
dc.identifier.citationJournal of Computer and System Sciences. 2018, 95: 19-34. doi:10.1016/j.jcss.2017.12.002es_ES
dc.identifier.issn0022-0000 (Print)-
dc.identifier.issn1090-2724 (Online)-
dc.description.abstractIn this paper we study the learning of graph languages. We extend the well-known classes of k-testability and k-testability in the strict sense languages to directed graph languages. We propose a grammatical inference algorithm to learn the class of directed acyclic k-testable in the strict sense graph languages. The algorithm runs in polynomial time and identifies this class of languages from positive data. We study its efficiency under several criteria, and perform a comprehensive experimentation with four datasets to show the validity of the method. Many fields, from pattern recognition to data compression, can take advantage of these results.es_ES
dc.rights© 2017 Elsevier Inc.es_ES
dc.subjectGraph languageses_ES
dc.subjectGraph automataes_ES
dc.subjectGrammatical inferencees_ES
dc.subjectk-Testable languageses_ES
dc.subject.otherLenguajes y Sistemas Informáticoses_ES
dc.titleGrammatical inference of directed acyclic graph languages with polynomial time complexityes_ES
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