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    Haute école de gestion de Genève

    Convex formulations of radius-margin based support vector machines

    Huyen, Do ; Kalousis, Alexandre

    In: proceedings of the 30th International Conference on Machine Learning, 2013, Atlanta, USA, 2013, p. 169-177 pp.

    We consider Support Vector Machines (SVMs) learned together with linear transformations of the feature spaces on which they are applied. Under this scenario the radius of the smallest data enclosing sphere is no longer fixed. Therefore optimizing the SVM error bound by considering both the radius and the margin has the potential to deliver a tighter error bound. In this paper we present two novel...