Robust multiclass kernel-based classifiers

Budi Santosa, Theodore B. Trafalis*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


In this research, a robust optimization approach applied to multiclass support vector machines (SVMs) is investigated. Two new kernel based-methods are developed to address data with input uncertainty where each data point is inside a sphere of uncertainty. The models are called robust SVM and robust feasibility approach model (Robust-FA) respectively. The two models are compared in terms of robustness and generalization error. The models are compared to robust Minimax Probability Machine (MPM) in terms of generalization behavior for several data sets. It is shown that the Robust-SVM performs better than robust MPM.

Original languageEnglish
Pages (from-to)261-279
Number of pages19
JournalComputational Optimization and Applications
Issue number2
Publication statusPublished - Nov 2007


  • Feasibility approach
  • Generalization error
  • Kernel
  • Minimax probability machine
  • Multiclass
  • Robust
  • Support vector machine
  • Uncertainty


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