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Sample selection via clustering to construct support vector-like classifiers

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dc.affiliation.dptoUC3M. Departamento de Teoría de la Señal y Comunicacioneses
dc.contributor.authorLyhyaoui Ben Yahia, Abdelouahid
dc.contributor.authorMartínez-Ramón, Manel
dc.contributor.authorMora-Jiménez, Inmaculada
dc.contributor.authorVázquez, Maryan
dc.contributor.authorSancho, José-Luis
dc.contributor.authorFigueiras, Aníbal
dc.date.accessioned2010-03-11T09:35:57Z
dc.date.available2010-03-11T09:35:57Z
dc.date.issued1999-11
dc.description.abstractThis paper explores the possibility of constructing RBF classifiers which, somewhat like support vector machines, use a reduced number of samples as centroids, by means of selecting samples in a direct way. Because sample selection is viewed as a hard computational problem, this selection is done after a previous vector quantization: this way obtaining also other similar machines using centroids selected from those that are learned in a supervised manner. Several forms of designing these machines are considered, in particular with respect to sample selection; as well as some different criteria to train them. Simulation results for well-known classification problems show very good performance of the corresponding designs, improving that of support vector machines and reducing substantially their number of units. This shows that our interest in selecting samples (or centroids) in an efficient manner is justified. Many new research avenues appear from these experiments and discussions, as suggested in our conclusions.
dc.description.statusPublicado
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationIEEE Transactions on Neural Networks, Vol. 10, n. 6,p.1474 - 1481. Nov. 1999
dc.identifier.doi10.1109/72.809092
dc.identifier.issn1045-9227
dc.identifier.publicationfirstpage1474
dc.identifier.publicationissue6
dc.identifier.publicationlastpage1481
dc.identifier.publicationtitleIEEE Transactions on Neural Networks
dc.identifier.publicationvolume10
dc.identifier.urihttps://hdl.handle.net/10016/7256
dc.language.isoeng
dc.publisherIEEE
dc.relation.publisherversionhttp://dx.doi.org/10.1109/72.809092
dc.rights© IEEE
dc.rights.accessRightsopen access
dc.subject.ecienciaTelecomunicaciones
dc.subject.otherSupport vector machines
dc.subject.otherSample selection
dc.subject.otherRadial basis functions
dc.subject.otherClustering
dc.titleSample selection via clustering to construct support vector-like classifiers
dc.typeresearch article*
dc.type.hasVersionVoR*
dc.type.reviewPeerReviewed
dspace.entity.typePublication
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