Please use this identifier to cite or link to this item: http://repositorio.inesctec.pt/handle/123456789/7194
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dc.contributor.authorSousa,Ren
dc.contributor.authorDa Rocha Neto,ARen
dc.contributor.authorBarreto,GAen
dc.contributor.authorJaime Cardosoen
dc.contributor.authorCoimbra,MTen
dc.date.accessioned2018-01-21T16:05:26Z-
dc.date.available2018-01-21T16:05:26Z-
dc.date.issued2014en
dc.identifier.urihttp://repositorio.inesctec.pt/handle/123456789/7194-
dc.description.abstractIn this paper we introduce a new conceptualization for the reduction of the number of support vectors (SVs) for an efficient design of support vector machines. The techniques here presented provide a good balance between SVs reduction and generalization capability. Our proposal explores concepts from classification with reject option. These methods output a third class (the rejected instances) for a binary problem when a prediction cannot be given with sufficient confidence. Rejected instances along with misclassified ones are discarded from the original data to give rise to a classification problem that can be linearly solved. Our experimental study on two benchmark datasets show significant gains in terms of SVs reduction with competitive performances.en
dc.languageengen
dc.relation3889en
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.titleReject option paradigm for the reduction of support vectorsen
dc.typeconferenceObjecten
dc.typePublicationen
Appears in Collections:CTM - Articles in International Conferences

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