Abstract
Twin support vector regression (TSVR) was proposed recently as a novel regressor that tries to find a pair of nonparallel planes, i.e., ε-insensitive up- and down-bounds, by solving two related SVM-type problems. However, it may incur suboptimal solution since its objective function is positive semi-definite and the lack of complexity control. In order to address this shortcoming, we develop a novel SVR algorithm termed as smooth twin SVR (STSVR). The idea is to reformulate TSVR as a strongly convex problem, which results in unique global optimal solution for each subproblem. To solve the proposed optimization problem, we first adopt a smoothing technique to convert the original constrained quadratic programming problems into unconstrained minimization problems, and then use the well-known Newton–Armijo algorithm to solve the smooth TSVR. The effectiveness of the proposed method is demonstrated via experiments on synthetic and real-world benchmark datasets.
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Chen, X., Yang, J., Liang, J. et al. Smooth twin support vector regression. Neural Comput & Applic 21, 505–513 (2012). https://doi.org/10.1007/s00521-010-0454-9
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DOI: https://doi.org/10.1007/s00521-010-0454-9