Papers by Alexander Yasuji Binder
We propose a localized approach to multiple kernel learning that, in contrast to prevalent approa... more We propose a localized approach to multiple kernel learning that, in contrast to prevalent approaches, can be formulated as a convex optimization problem over a given cluster structure. From which we obtain the first generalization error bounds for localized multiple kernel learning and derive an efficient optimization algorithm based on the Fenchel dual representation. Experiments on real-world datasets from the application domains of computational biology and computer vision show that the convex approach to localized multiple kernel learning can achieve higher prediction accuracies than its global and non-convex local counterparts.
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Papers by Alexander Yasuji Binder