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• W892996005 abstract "We study Nystrom type subsampling approaches to large scale kernel methods, and prove learning bounds in the statistical learning setting, where random sampling and high probability estimates are considered. In particular, we prove that these approaches can achieve optimal learning bounds, provided the subsampling level is suitably chosen. These results suggest a simple incremental variant of Nystrom Kernel Regularized Least Squares, where the subsampling level implements a form of computational regularization, in the sense that it controls at the same time regularization and computations. Extensive experimental analysis shows that the considered approach achieves state of the art performances on benchmark large scale datasets." @default.
• W892996005 created "2016-06-24" @default.
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• W892996005 date "2015-07-16" @default.
• W892996005 modified "2023-10-03" @default.
• W892996005 title "Less is More: Nystrom Computational Regularization" @default.
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• W892996005 hasPublicationYear "2015" @default.
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