J.-B. Pothin

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Kernel methods are well known standard tools for solving function approximation and pattern classification problems. In this paper, we consider online learning in a reproducing kernel Hilbert space. We develop a simple and computationally efficient algorithm for sparse solutions. The approach is based on sequential projection learning and the coherence(More)
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