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A Geometric Interpretation of v-SVM Classifiers
Neural Information Processing Systems
We show that the recently proposed variant of the Support Vector machine (SVM) algorithm, known as v-SVM, can be interpreted as a maximal separation between subsets of the convex hulls of the data, which we call soft convex hulls. The soft convex hulls are controlled by choice of the parameter v. If the intersection of the convex hulls is empty, the hyperplane is positioned halfway between them such that the distance between convex hulls, measured along the normal, is maximized; and if it isdblp:conf/nips/CrispB99 fatcat:jybpefyfi5f6bijt26uheantpa