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Improving the training time of support vector regression algorithms through novel hyper-parameters search space reductions

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NEUROCOMPUTING 2009年第16-18期72卷 3683-3691页

作者： Ortiz-Garcia, Emilio G. Salcedo-Sanz, Sancho Perez-Bellido, Angel M. Portilla-Figueras, Jose A. Univ Alcala de Henares Dept Signal Theory & Commun Grp Heurist Modernos Optimizac & Diseno Redes GHE Madrid 28871 Spain

The selection of hyper-parameters in support vector machines (svm) is a key point in the training process of these models when applied to regression problems. Unfortunately, an exact method to obtain the optimal set of svm hyper-parameters is unknown, and search algorithms are usually applied to obtain the best possible set of hyper-parameters. In general these search algorithms are implemented as grid searches, which are time consuming, so the computational cost of the svm training process increases considerably. This paper presents a novel study of the effect of including reductions in the range of svm hyper-parameters, in order to reduce the svm training time, but with the minimum possible impact in its performance. The paper presents reduction in parameter C, by considering its relation with the rest of svm hyper-parameters (gamma and epsilon), through an approximation of the svm model. On the other hand, we use some characteristics of the Gaussian kernel function and a previous result in the literature to obtain novel bounds for gamma and epsilon hyper-parameters. The search space reductions proposed are evaluated in different regression problems from UCI and StatLib databases. All the experiments carried out applying the popular LIBsvm solver have shown that our approach reduces the svm training time, maintaining the svm performance similar to when the complete range in svm parameters is considered. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Support vector regression algorithms svm hyper-parameters Training time Search space reductions

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