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dc.contributor.authorGomez Guillen, David
dc.contributor.authorRojas Espinosa, Alfonso
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Telemàtica
dc.date.accessioned2016-01-22T17:13:09Z
dc.date.available2016-03-01T01:30:46Z
dc.date.issued2016-01-01
dc.identifier.citationGomez, D., Rojas, A. An empirical overview of the No Free Lunch Theorem and its effect on Real-World Machine Learning Classification. "Neural computation", 01 Gener 2016, vol. 28, núm. 1, p. 216-228.
dc.identifier.issn0899-7667
dc.identifier.urihttp://hdl.handle.net/2117/81906
dc.description.abstractA sizable amount of research has been done to improve the mechanisms for knowledge extraction such as machine learning classification or regression. Quite unintuitively, the no free lunch (NFL) theorem states that all optimization problem strategies perform equally well when averaged over all possible problems. This fact seems to clash with the effort put forth toward better algorithms. This letter explores empirically the effect of the NFL theorem on some popular machine learning classification techniques over real-world data sets.
dc.format.extent13 p.
dc.language.isoeng
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
dc.subject.lcshNeural computers
dc.subject.lcshMachine learning
dc.subject.otherMachine learning
dc.titleAn empirical overview of the No Free Lunch Theorem and its effect on Real-World Machine Learning Classification
dc.typeArticle
dc.subject.lemacOrdinadors neuronals
dc.subject.lemacAprenentatge automàtic
dc.contributor.groupUniversitat Politècnica de Catalunya. GRXCA - Grup de Recerca en Xarxes de Comunicacions Cel·lulars i Ad-hoc
dc.identifier.doi10.1162/NECO_a_00793
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://www.mitpressjournals.org/doi/abs/10.1162/NECO_a_00793?journalCode=neco#.VqJe-U-QncN
dc.rights.accessOpen Access
drac.iddocument17390929
dc.description.versionPostprint (published version)
upcommons.citation.authorGomez, D., Rojas, A.
upcommons.citation.publishedtrue
upcommons.citation.publicationNameNeural computation
upcommons.citation.volume28
upcommons.citation.number1
upcommons.citation.startingPage216
upcommons.citation.endingPage228


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