A mixed quadratic programming model for a robust support vector machine - Serna-Diaz, Raquel (2021)
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.