A novel multi-class SVM model using second-order cone constraints

Julio López, Sebastián Maldonado, Miguel Carrasco

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

13 Citas (Scopus)


In this work we present a novel maximum-margin approach for multi-class Support Vector Machines based on second-order cone programming. The proposed method consists of a single optimization model to construct all classification functions, in which the number of second-order cone constraints corresponds to the number of classes. This is a key difference from traditional SVM, where the number of constraints is usually related to the number of training instances. This formulation is extended further to kernel-based classification, while the duality theory provides an interesting geometric interpretation: the method finds an equidistant point between a set of ellipsoids. Experiments on benchmark datasets demonstrate the virtues of our method in terms of predictive performance compared with various other multicategory SVM approaches.
Idioma originalInglés estadounidense
Páginas (desde-hasta)457-469
Número de páginas13
PublicaciónApplied Intelligence
EstadoPublicada - 1 mar. 2016

Palabras clave

  • Multi-class classification
  • Second-order cone programming
  • Support vector machines


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