Time–Adaptive Support Vector Machines
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 oth...
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| Autores principales: | , , |
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| Formato: | Artículo |
| Lenguaje: | en_US |
| Publicado: |
Asociación Española de Inteligencia Artificial
2011
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| Materias: | |
| Acceso en línea: | http://hdl.handle.net/2133/1718 http://hdl.handle.net/2133/1718 |
| Aporte de: |
| Sumario: | 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. |
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