Support Vector Machine

Robust support vector machines via conic optimization

  • Shaoning Han
  • Andrés Gómez
    • Categories (Mixed) Integer Nonlinear Programming, Cone Programming, Optimization in Data Science Tags , , , ,

    We consider the problem of learning support vector machines robust to uncertainty. It has been established in the literature that typical loss functions, including the hinge loss, are sensible to data perturbations and outliers, thus performing poorly in the setting considered. In contrast, using the 0-1 loss or a suitable non-convex approximation results in robust … Read more

    Robust and Distributionally Robust Optimization Models for Support Vector Machine

  • Daniel Faccini
  • Francesca Maggioni
  • Florian A. Potra
    • Categories Applications - OR and Management Sciences, Robust Optimization Tags , , , ,

    In this paper we present novel data-driven optimization models for Support Vector Machines (SVM), with the aim of linearly separating two sets of points that have non-disjoint convex closures. Traditional classi cation algorithms assume that the training data points are always known exactly. However, real-life data are often subject to noise. To handle such uncertainty, we … Read more