Time–Adaptive Support Vector Machines

dc.contributor.author	Grinblat, Guillermo
dc.contributor.author	Granitto, Pablo M.
dc.contributor.author	Ceccatto, Alejandro
dc.date.accessioned	2011-03-11T13:46:59Z
dc.date.available	2011-03-11T13:46:59Z
dc.date.issued	2008
dc.description.abstract	In this work we propose an adaptive classification method able both to learn and to follow the temporal evolution of a drifting concept. With that purpose we introduce a modified SVM classifier, created using multiple hyperplanes valid only at small temporal intervals (windows). In contrast to other strategies proposed in the literature, our method learns all hyperplanes in a global way, minimizing a cost function that evaluates the error committed by this family of local classifiers plus a measure associated to the VC dimension of the family. We also show how the idea of slowly changing classifiers can be applied to non-linear stationary concepts with results similar to those obtained with normal SVMs using gaussian kernels.	es
dc.description.peerreviewed	Peer reviewed	es
dc.identifier.issn	1137-3601
dc.identifier.uri	http://hdl.handle.net/2133/1718
dc.language.iso	en_US	es
dc.publisher	Asociación Española de Inteligencia Artificial	es
dc.relation.publisherversion	http://www.aepia.org/	es
dc.rights	Open access	es
dc.rights.text	© AEPIA	es
dc.subject	Adaptive methods	es
dc.subject	Support Vector Machine	es
dc.subject	Drifting concepts	es
dc.title	Time–Adaptive Support Vector Machines	es
dc.type	Article	es

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