Improving multiclass pattern recognition by the combination of two strategies

dc.contributor.author	Ortiz-Boyer, Domingo
dc.contributor.author	García-Pedrajas, Nicolás
dc.date.accessioned	2010-12-28T12:10:49Z
dc.date.available	2010-12-28T12:10:49Z
dc.date.issued	2006
dc.identifier.issn	0162-8828
dc.identifier.uri	http://hdl.handle.net/10396/3949
dc.description.abstract	We 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.mimetype	application/pdf
dc.language.iso	eng	en
dc.publisher	IEEE	en
dc.rights	https://creativecommons.org/licenses/by-nc-nd/4.0/	es_ES
dc.source	Ieee Transactions on Pattern Analysis and Machine Intelligence 28 (6), 1001-1006 (2006)	en
dc.subject	Support Vector Machines	en
dc.subject	One-Vs-One	en
dc.subject	One-Vs-All	en
dc.subject	Neural Networks	en
dc.title	Improving multiclass pattern recognition by the combination of two strategies	en
dc.type	info:eu-repo/semantics/article	en
dc.rights.accessRights	info:eu-repo/semantics/openAccess	es_ES