"A second-order cone programming formulation for twin support vector machines"

Research areas:
  • Uncategorized
Type of Publication:
Support vector classification,Twin support vector machines,Second order cone programming
  • S. Maldonado
  • J. López
Applied Intelligence
Second-order cone programming (SOCP) formulations have received increasing attention as robust optimization schemes for Support Vector Machine (SVM) classification. These formulations study the worst-case setting for class-conditional densities, leading to potentially more effective classifiers in terms of performance compared to the standard SVM formulation. In this work we propose an SOCP extension for Twin SVM, a recently developed classification approach that constructs two nonparallel classifiers. The linear and kernel-based SOCP formulations for Twin SVM are derived, while the duality analysis provides interesting geometrical properties of the proposed method. Experimentsonbenchmarkdatasetsdemonstratethevirtues of our approach in terms of classification performance compared to alternative SVM methods.
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