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Ellipsoidal support vector regression based on second-order cone programming
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DOI：10.1016/j.neucom.2018.04.035.png)
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摘要
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In this paper, we propose a novel method for Support Vector Regression (SVR) based on second-order cones. The proposed approach defines a robust worst-case framework for the conditional densities of the input data. Linear and kernel-based second-order cone programming formulations for SVR are proposed, while the duality theory allows us to derive interesting geometrical properties for this strategy: the method maximizes the margin between two ellipsoids obtained by shifting the response variable up and down by a fixed parameter. Experiments for regression on twelve well-known datasets confirm the superior performance of our proposal compared to alternative methods such as standard SVR and linear regression. (C) 2018 Elsevier B.V. All rights reserved.
Keyword:
Support vector regression
Robust optimization
Second-order cone programming
Kernel methods
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期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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