A mixed quadratic programming model for a robust support vector machine


  • Raquel Serna-Diaz Facultad de Ciencias, Universidad Nacional Agraria la Molina, La molina, Lima, Perú.
  • Raimundo Santos Leite Instituto de Ciencias Exatas e Biológicas , Universidade Federal de Ouro Preto, Campus UniversitárioMorro do Cruzeiro, CEP:35400-000, Ouro Preto, MG, Brasil.
  • Paulo J. S. Silva Instituto de Matemática, Estatística e Computação Científica, Universidade Estadual de Campinas, Rua Sérgio Buarque de Holanda, 651 13083-859, Campinas, SP, Brasil.


SVM, Mixed integer quadratic programming, Outliers, Classification


Support Vector Machines are extensively used to solve classification problems in Pattern Recognition. They deal with small errors in the training data using the concept of soft margin, that allowfor imperfect classification.

However, if the training data have systematic errors or outliers such strategy is not robust resulting in bad generalization. In this paper we present a model for robust Support Vector Machine classification that can automatically ignore spurius data. We show then that the model can be solved using a high performance Mixed Integer Quadratic Programming solver and present preliminary numerical experiments using real world data that looks promissing.


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How to Cite

Serna-Diaz, R., Santos Leite, R., & S. Silva, P. J. (2021). A mixed quadratic programming model for a robust support vector machine. Selecciones Matemáticas, 8(01), 27-36. Retrieved from https://revistas.unitru.edu.pe/index.php/SSMM/article/view/4531