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dc.contributor.authorOrtiz-Boyer, Domingo
dc.contributor.authorGarcía-Pedrajas, Nicolás
dc.date.accessioned2010-12-28T12:10:49Z
dc.date.available2010-12-28T12:10:49Z
dc.date.issued2006
dc.identifier.issn0162-8828
dc.identifier.urihttp://hdl.handle.net/10396/3949
dc.description.abstractWe present a new method of multiclass classification based on the combination of one- vs- all method and a modification of one- vs- one method. This combination of one- vs- all and one- vs- one methods proposed enforces the strength of both methods. A study of the behavior of the two methods identifies some of the sources of their failure. The performance of a classifier can be improved if the two methods are combined in one, in such a way that the main sources of their failure are partially avoided.en
dc.format.mimetypeapplication/pdf
dc.language.isoengen
dc.publisherIEEEen
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/es_ES
dc.sourceIeee Transactions on Pattern Analysis and Machine Intelligence 28 (6), 1001-1006 (2006)en
dc.subjectSupport Vector Machinesen
dc.subjectOne-Vs-Oneen
dc.subjectOne-Vs-Allen
dc.subjectNeural Networksen
dc.titleImproving multiclass pattern recognition by the combination of two strategiesen
dc.typeinfo:eu-repo/semantics/articleen
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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