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arXiv · 1001.2094

Regularization in kernel learning

Shahar Mendelson·Joseph Neeman
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Abstract

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.

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Shahar Mendelson, Joseph Neeman. 2010-01-13. Regularization in kernel learning. https://doi.org/10.1214/09-aos728

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