Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6863787 | Neurocomputing | 2018 | 28 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Sebastián Maldonado, Julio López,