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dc.contributor.authorShafti, Leila Shila
dc.contributor.authorPérez, Eduardo
dc.contributor.otherUAM. Departamento de Ingeniería Informáticaes_ES
dc.date.accessioned2015-02-26T17:56:29Z
dc.date.available2015-02-26T17:56:29Z
dc.date.issued2004
dc.identifier.citationAdvances in Artificial Intelligence – IBERAMIA 2004: 9th Ibero-American Conference on AI, Puebla, Mexico, November 22-26, 2004. Proceedings. Lecture Notes in Computer Science, Volumen 3315. Springer, 2004. 246-255en_US
dc.identifier.isbn978-3-540-30498-2 (online)en_US
dc.identifier.isbn978-3-540-23806-5 (print)en_US
dc.identifier.issn0302-9743 (print)en_US
dc.identifier.issn1611-3349 (online)en_US
dc.identifier.urihttp://hdl.handle.net/10486/664130
dc.descriptionThe final publication is available at Springer via http://dx.doi.org/10.1007/978-3-540-30498-2_25en_US
dc.descriptionProceedings of 9th Ibero-American Conference on AI, Puebla, Mexico, November 22-26, 2004.en_US
dc.description.abstractConstructive Induction methods aim to solve the problem of learning hard concepts despite complex interaction in data. We propose a new Constructive Induction method based on Genetic Algorithms with a non-algebraic representation of features. The advantage of our method to some other similar methods is that it constructs and evaluates a combination of features. Evaluating constructed features together, instead of considering them one by one, is essential when number of interacting attributes is high and there are more than one interaction in concept. Our experiments show the effectiveness of this method to learn such concepts.en_US
dc.description.sponsorshipThis work has been partially supported by the Spanish Interdepartmental Commission for Science and Technology (CICYT), under Grant number TIC2002-1948en_US
dc.format.extent11 pág.es_ES
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherSpringer Berlin Heidelberg
dc.relation.ispartofLecture Notes in Computer Scienceen_US
dc.rights© Springer-Verlag Berlin Heidelberg 2004
dc.subject.otherArtificial Intelligenceen_US
dc.subject.otherComputation by Abstract Devicesen_US
dc.subject.otherImage Processing and Computer Visionen_US
dc.titleMachine learning by multi-feature extraction using genetic algorithmsen_US
dc.typeconferenceObjecten
dc.typebookParten
dc.subject.ecienciaInformáticaes_ES
dc.relation.publisherversionhttp://dx.doi.org/10.1007/978-3-540-30498-2_25
dc.identifier.doi10.1007/978-3-540-30498-2_25
dc.identifier.publicationfirstpage246
dc.identifier.publicationlastpage255
dc.identifier.publicationvolume3315
dc.relation.eventdateNovember 22-26, 2004en_US
dc.relation.eventnumber9
dc.relation.eventplacePuebla (Mexico)en_US
dc.relation.eventtitle9th Ibero-American Conference on Artificial Intelligence, IBERAMIA 2004en_US
dc.type.versioninfo:eu-repo/semantics/acceptedVersionen
dc.contributor.groupHerramientas Interactivas Avanzadas (ING EPS-003)es_ES
dc.rights.accessRightsopenAccessen
dc.authorUAMShafti , Leila Shila (261155)
dc.facultadUAMEscuela Politécnica Superior


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