An up-to-date account of the interplay between optimization and machine learning,
accessible to students and researchers in both communities. The interplay between …

Robust optimization is still a relatively new approach to optimization problems affected by
uncertainty, but it has already proved so useful in real applications that it is difficult to tackle …

We consider regularized support vector machines (SVMs) and show that they are precisely
equivalent to a new robust optimization formulation. We show that this equivalence of robust …

Robust Optimization is a rapidly develo** methodology for handling optimization
problems affected by non-stochastic “uncertain-but-bounded” data perturbations. In this …

We propose a novel second order cone programming formulation for designing robust
classifiers which can handle uncertainty in observations. Similar formulations are also …

Support vector machine (SVM) is a powerful model for supervised learning. This article
addresses the nonlinear binary classification problem using kernel-based SVM with …

Robust optimization is a common optimization framework under uncertainty when problem
parameters are unknown, but it is known that they belong to some given uncertainty set. In …

This paper gives an overview of developments in the field of robust optimization in machine
learning (ML) in general and Support Vector Machine (SVM)/Support Vector Regression …

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 …

Despite important advances in microarray-based molecular classification of tumors, its
application in clinical settings remains formidable. This is in part due to the limitation of …