Convexity, Classification, and Risk BoundsConvexity, Classification, and Risk Bounds Peter L. BARTLETT, Michael I. JORDAN, and Jon D. MCAULIFFE Many of the classification algorithms developed in the machine learning literature, including the support vector machine and boosting, can be viewed as minimum contrast methods that minimize a convex surrogate of the 0–1 loss function. The convexity makes these algorithms computationally efficient
