Time–Adaptive Support Vector Machines
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Date
2008
Authors
Grinblat, Guillermo
Granitto, Pablo M.
Ceccatto, Alejandro
Journal Title
Journal ISSN
Volume Title
Publisher
Asociación Española de Inteligencia Artificial
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.
Description
Keywords
Adaptive methods, Support Vector Machine, Drifting concepts