"Multi-class second-order cone programming support vector machines"

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Research areas:
Year:
2016
Type of Publication:
Article
Keywords:
Multi-class classification, Support vector machines, Second order cone programming, Quadratic programming, Convex optimization
Authors:
  • J. Lopez
  • S. Maldonado
Journal:
Information Sciences
Volume:
330
Pages:
328-341
Month:
February
ISSN:
0020-0255
Abstract:
This paper presents novel second-order cone programming (SOCP) formulations that determine a linear multi-class predictor using support vector machines (SVMs). We first extend the ideas of OvO (One-versus-One) and OvA (One-versus-All) SVM formulations to SOCP-SVM, providing two interesting alternatives to the standard SVM formulations. Additionally, we propose a novel approach (MC-SOCP) that simultaneously constructs all required hyperplanes for multi-class classification, based on the multi-class SVM formulation (MC-SVM). The use of conic constraints for each pair of training patterns in a single optimization problem provides an adequate framework for a balanced and effective prediction.