Grinblat, GuillermoGranitto, Pablo M.Ceccatto, Alejandro2011-03-112011-03-1120081137-3601http://hdl.handle.net/2133/1718In 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.en-USOpen accessAdaptive methodsSupport Vector MachineDrifting conceptsArticle© AEPIA