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

Julio López, Sebastián Maldonado, Miguel Carrasco

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


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.
Original languageAmerican English
Pages (from-to)457-469
Number of pages13
JournalApplied Intelligence
Issue number2
StatePublished - 1 Mar 2016


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


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