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The Support Vector Machine error bound is a function of the margin and radius. Standard SVM algorithms maximize the margin within a given feature space, therefore the radius is fixed and thus ignored in the optimization. We propose an extension of the standard SVM optimization in which we also accou ...

A serious drawback of kernel methods, and Support Vector Machines (SVM) in particular, is the difficulty in choosing a suitable kernel function for a given dataset. One of the approaches proposed to address this problem is Multiple Kernel Learning (MKL) in which several kernels are combined adaptive ...