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Neural Computing and Applications

Erschienen in:

01.04.2012 | Original Article

Smooth twin support vector regression

verfasst von: Xiaobo Chen, Jian Yang, Jun Liang, Qiaolin Ye

Erschienen in: Neural Computing and Applications | Ausgabe 3/2012

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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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Titel: Smooth twin support vector regression
verfasst von: Xiaobo Chen
Jian Yang
Jun Liang
Qiaolin Ye
Publikationsdatum: 01.04.2012
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 3/2012
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-010-0454-9

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