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- W2949779530 abstract "We study generalization properties of kernel regularized least squares regression based on a partitioning approach. We show that optimal rates of convergence are preserved if the number of local sets grows sufficiently slowly with the sample size. Moreover, the partitioning approach can be efficiently combined with local Nystrom subsampling, improving computational cost twofold." @default.
- W2949779530 created "2019-06-27" @default.
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- W2949779530 date "2017-07-11" @default.
- W2949779530 modified "2023-09-27" @default.
- W2949779530 title "Reducing training time by efficient localized kernel regression" @default.
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