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Machine learning by multi-feature extraction using genetic algorithms
dc.contributor.author | Shafti, Leila Shila | |
dc.contributor.author | Pérez, Eduardo | |
dc.contributor.other | UAM. Departamento de Ingeniería Informática | es_ES |
dc.date.accessioned | 2015-02-26T17:56:29Z | |
dc.date.available | 2015-02-26T17:56:29Z | |
dc.date.issued | 2004 | |
dc.identifier.citation | Advances 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. 246-255 | en_US |
dc.identifier.isbn | 978-3-540-30498-2 (online) | en_US |
dc.identifier.isbn | 978-3-540-23806-5 (print) | en_US |
dc.identifier.issn | 0302-9743 (print) | en_US |
dc.identifier.issn | 1611-3349 (online) | en_US |
dc.identifier.uri | http://hdl.handle.net/10486/664130 | |
dc.description | The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-540-30498-2_25 | en_US |
dc.description | Proceedings of 9th Ibero-American Conference on AI, Puebla, Mexico, November 22-26, 2004. | en_US |
dc.description.abstract | Constructive 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.sponsorship | This work has been partially supported by the Spanish Interdepartmental Commission for Science and Technology (CICYT), under Grant number TIC2002-1948 | en_US |
dc.format.extent | 11 pág. | es_ES |
dc.format.mimetype | application/pdf | en |
dc.language.iso | eng | en |
dc.publisher | Springer Berlin Heidelberg | |
dc.relation.ispartof | Lecture Notes in Computer Science | en_US |
dc.rights | © Springer-Verlag Berlin Heidelberg 2004 | |
dc.subject.other | Artificial Intelligence | en_US |
dc.subject.other | Computation by Abstract Devices | en_US |
dc.subject.other | Image Processing and Computer Vision | en_US |
dc.title | Machine learning by multi-feature extraction using genetic algorithms | en_US |
dc.type | conferenceObject | en |
dc.type | bookPart | en |
dc.subject.eciencia | Informática | es_ES |
dc.relation.publisherversion | http://dx.doi.org/10.1007/978-3-540-30498-2_25 | |
dc.identifier.doi | 10.1007/978-3-540-30498-2_25 | |
dc.identifier.publicationfirstpage | 246 | |
dc.identifier.publicationlastpage | 255 | |
dc.identifier.publicationvolume | 3315 | |
dc.relation.eventdate | November 22-26, 2004 | en_US |
dc.relation.eventnumber | 9 | |
dc.relation.eventplace | Puebla (Mexico) | en_US |
dc.relation.eventtitle | 9th Ibero-American Conference on Artificial Intelligence, IBERAMIA 2004 | en_US |
dc.type.version | info:eu-repo/semantics/acceptedVersion | en |
dc.contributor.group | Herramientas Interactivas Avanzadas (ING EPS-003) | es_ES |
dc.rights.accessRights | openAccess | en |
dc.authorUAM | Shafti , Leila Shila (261155) | |
dc.facultadUAM | Escuela Politécnica Superior |