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calendar_month Publicación: 14/09/2005

Linear penalization support vector machines for feature selection

Autor: Jaime Miranda , Richard Weber , Ricardo Montoya

Profesor Relacionado: Ricardo Montoya

Support Vector Machines have proved to be powerful tools for classification tasks combining the minimization of classification errors and maximizing their generalization capabilities. Feature selection, however, is not considered explicitly in the basic model formulation. We propose a linearly penalized Support Vector Machines (LP-SVM) model where feature selection is performed simultaneously with model construction. Its application to a problem of customer retention and a comparison with other feature selection techniques demonstrates its effectiveness.

Fuente: Lecture Notes in Computer Science

3776, 188-192

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