Examinando por Autor "Ceccatto, Alejandro"

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    ÍtemAcceso Abierto
    Time–Adaptive Support Vector Machines
    (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.