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- W2952392021 abstract "We consider regularized support vector machines (SVMs) and show that they are precisely equivalent to a new robust optimization formulation. We show that this equivalence of robust optimization and regularization has implications for both algorithms, and analysis. In terms of algorithms, the equivalence suggests more general SVM-like algorithms for classification that explicitly build in protection to noise, and at the same time control overfitting. On the analysis front, the equivalence of robustness and regularization, provides a robust optimization interpretation for the success of regularized SVMs. We use the this new robustness interpretation of SVMs to give a new proof of consistency of (kernelized) SVMs, thus establishing robustness as the reason regularized SVMs generalize well." @default.
- W2952392021 created "2019-06-27" @default.
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- W2952392021 date "2008-03-25" @default.
- W2952392021 modified "2023-09-27" @default.
- W2952392021 title "Robustness and Regularization of Support Vector Machines" @default.
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