Real adaboost with gate controlled fusion

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dc.contributor.authorMayhua López, Efraín Tito
dc.contributor.authorGómez Verdejo, Vanessa
dc.contributor.authorFigueiras-Vidal, Anibal
dc.date.accessioned2019-01-29T22:19:55Z
dc.date.available2019-01-29T22:19:55Z
dc.date.issued2012
dc.description.abstractIn this brief, we propose to increase the capabilities of standard real AdaBoost (RAB) architectures by replacing their linear combinations with a fusion controlled by a gate with fixed kernels. Experimental results in a series of well-known benchmark problems support the effectiveness of this approach in improving classification performance. Although the need for cross-validation processes obviously leads to higher training requirements and more computational effort, the operation load is never much higher; in many cases it is even lower than that of competitive RAB schemes. © 2012 IEEE.es_PE
dc.description.uriTrabajo académicoes_PE
dc.identifier.doihttps://doi.org/10.1109/TNNLS.2012.2219318es_PE
dc.identifier.issn2162237Xes_PE
dc.identifier.urihttps://hdl.handle.net/20.500.12590/15881
dc.language.isoenges_PE
dc.publisherScopuses_PE
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84876902833&doi=10.1109%2fTNNLS.2012.2219318&partnerID=40&md5=efb57382c29fc8be71bdee5d1ad3053bes_PE
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_PE
dc.sourceRepositorio Institucional - UCSPes_PE
dc.sourceUniversidad Católica San Pabloes_PE
dc.sourceScopuses_PE
dc.subjectBench-mark problemses_PE
dc.subjectClassification performancees_PE
dc.subjectComputational effortes_PE
dc.subjectControlled fusiones_PE
dc.subjectensembleses_PE
dc.subjectLinear combinationses_PE
dc.subjectMixtures of expertses_PE
dc.subjectTraining requirementes_PE
dc.subjectBenchmarkinges_PE
dc.subjectClassification (of information)es_PE
dc.subjectDirect energy conversiones_PE
dc.subjectNeural networkses_PE
dc.subjectAdaptive boostinges_PE
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#2.02.01es_PE
dc.titleReal adaboost with gate controlled fusiones_PE
dc.typeinfo:eu-repo/semantics/article
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