A multi-class SVM approach based on the l1-norm minimization of the distances between the reduced convex hulls.

Miguel Carrasco, Julio López, Sebastián Maldonado*

*Autor correspondiente de este trabajo

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

15 Citas (Scopus)

Resumen

Multi-class classification is an important pattern recognition task that can be addressed accurately and efficiently by Support Vector Machine (SVM). In this work we present a novel SVM-based multi-class classification approach based on the center of the configuration, a point which is equidistant to all classes. The center of the configuration is obtained from the dual formulation by minimizing the distances between the reduced convex hulls using the l1-norm, while the decision functions are subsequently constructed from this point. This work also extends the ideas of Zhou et al. (2002) [37] to multi-class classification. The use of l1-norm provides a single linear programming formulation, which reduces the complexity and confers scalability compared with other multi-class SVM methods based on quadratic programming formulations. Experiments on benchmark datasets demonstrate the virtues of our approach in terms of classification performance and running times compared with various other multi-class SVM methods.
Idioma originalInglés
Páginas (desde-hasta)1598-1607
Número de páginas10
PublicaciónPattern Recognition
Volumen48
N.º5
DOI
EstadoPublicada - 1 may. 2015

Nota bibliográfica

Publisher Copyright:
© 2014 Elsevier Ltd. All rights reserved.

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