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Erschienen in:
2006 | OriginalPaper | Buchkapitel
PSO-Based Hyper-Parameters Selection for LS-SVM Classifiers
verfasst von : X. C. Guo, Y. C. Liang, C. G. Wu, C. Y. Wang
Erschienen in: Neural Information Processing
Verlag: Springer Berlin Heidelberg
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The determination for hyper-parameters including kernel parameters and the regularization is important to the performance of least squares support vector machines (LS-SVMs). In this paper, the problem of model selection for LS-SVMs is discussed. The particle swarm optimization (PSO) is introduced to select the LS-SVMs hyper-parameters. In the proposed method we do not need to consider the analytic property of the generalization performance measure and the number of hyper-parameters. The feasibility of this method is evaluated on benchmark data sets. Experimental results show that better performance can be obtained. Moreover, different kinds of kernel families are investigated by using the proposed method. Experimental results also show that the best and good test performance could be obtained by using the SRBF and RBF kernel functions, respectively.