A new algorithm for computation of a regularization solution path for reinforced multicategory support vector machines

Xiao Xiao, Xiexin Liu, Xiaoling Lu, Xiangyu Chang, Yufeng Liu

Research output: Contribution to journalArticlepeer-review


The recently proposed Reinforced Multicategory Support Vector Machine (RMSVM) has been proven to have desirable theoretical properties as well as competitive numerical accuracy for multi-class classification problems. Currently solving the RMSVM is based on a grid search approach for selecting the tuning parameter λ, which dramatically increases its computational complexity. To overcome this hurdle we develop a new algorithm RMSVMPATH to compute a regularization solution path for RMSVM. We relax the commonly used continuity assumption and propose a new linear programming approach. Numerical simulations and real data analyses demonstrate that the proposed algorithm can yield a valid solution path at a low computational cost.

Original languageEnglish (US)
Pages (from-to)149-163
Number of pages15
JournalCanadian Journal of Statistics
Issue number2
StatePublished - Jun 2017

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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