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Regularization in kernel learning. (English) Zbl 1191.68356
Summary: Under mild assumptions on the kernel, we obtain the best known error rates in a regularized learning scenario taking place in the corresponding reproducing kernel Hilbert space (RKHS). The main novelty in the analysis is a proof that one can use a regularization term that grows significantly slower than the standard quadratic growth in the RKHS norm.

MSC:
68Q32Computational learning theory
60G99Stochastic processes
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References:
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