Reviews: Learning Confidence Sets using Support Vector Machines

Summary The paper proposes an SVM-like classification method for estimating sets containing a pre-specified amount of samples for each class. The overlap of these two sets is a region with ambiguity and should thus be small. The key results are: problem formulation and reformulation using a convex surrogate loss function. Impression The problem formulation is very interesting and the combination of theoretical and experimental results is above standard. In addition, the paper is easy to follow. My main concerns are: - What is the conceptional difference between the proposed approach and classification with reject option as in [2].