(Asociación Española de Inteligencia Artificial, 2008) Grinblat, Guillermo; Granitto, Pablo M.; Ceccatto, Alejandro
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.