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• W2950587470 abstract "We prove that, under low noise assumptions, the support vector machine with $Nll m$ random features (RFSVM) can achieve the learning rate faster than $O(1/sqrt{m})$ on a training set with $m$ samples when an optimized feature map is used. Our work extends the previous fast rate analysis of random features method from least square loss to 0-1 loss. We also show that the reweighted feature selection method, which approximates the optimized feature map, helps improve the performance of RFSVM in experiments on a synthetic data set." @default.
• W2950587470 created "2019-06-27" @default.
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• W2950587470 date "2018-09-12" @default.
• W2950587470 modified "2023-09-27" @default.
• W2950587470 title "But How Does It Work in Theory? Linear SVM with Random Features" @default.
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