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• W603680107 abstract "The support vector machine (SVM) is a flexible classification method that accommodates a kernel trick to learn nonlinear decision rules. The traditional formulation as an optimization problem is a quadratic program. In efforts to reduce computational complexity, some have proposed using an L1-norm regularization to create a linear program (LP). In other efforts aimed at increasing the robustness to outliers, investigators have proposed using the ramp loss which results in what may be expressed as a quadratic integer programming problem (QIP). In this paper, we consider combining these ideas for ramp loss SVM with L1-norm regularization. The result is four formulations for SVM that each may be expressed as a mixed integer linear program (MILP). We observe that ramp loss SVM with L1-norm regularization provides robustness to outliers with the linear kernel. We investigate the time required to find good solutions to the various formulations using a branch and bound solver." @default.
• W603680107 created "2016-06-24" @default.
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• W603680107 date "2015-01-11" @default.
• W603680107 modified "2023-09-25" @default.
• W603680107 title "The Support Vector Machine and Mixed Integer Linear Programming: Ramp Loss SVM with L1-Norm Regularization" @default.
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• W603680107 doi "https://doi.org/10.1287/ics.2015.0017" @default.
• W603680107 hasPublicationYear "2015" @default.
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