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• W1754455042 abstract "Regularized risk minimization with the binary hinge loss and its variants lies at the heart of many machine learning problems. Bundle methods for regularized risk minimization (BMRM) and the closely related SVMStruct are considered the best general purpose solvers to tackle this problem. It was recently shown that BMRM requires $O(1/epsilon)$ iterations to converge to an $epsilon$ accurate solution. In the first part of the paper we use the Hadamard matrix to construct a regularized risk minimization problem and show that these rates cannot be improved. We then show how one can exploit the structure of the objective function to devise an algorithm for the binary hinge loss which converges to an $epsilon$ accurate solution in $O(1/sqrt{epsilon})$ iterations." @default.
• W1754455042 created "2016-06-24" @default.
• W1754455042 creator A5013623933 @default.
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• W1754455042 date "2009-09-07" @default.
• W1754455042 modified "2023-09-27" @default.
• W1754455042 title "Lower Bounds for BMRM and Faster Rates for Training SVMs" @default.
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• W1754455042 hasPublicationYear "2009" @default.
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