We propose practical recommendations for selecting metaparameters for SVM regression (that is, ε -insensitive zone and regularization parameter C). The proposed methodology advocates analytic parameter selection directly from the training data, rather than resampling approaches commonly used in SVM applications. Good generalization performance of the proposed parameter selection is demonstrated empirically using several lowdimensional and high-dimensional regression problems. In addition, we compare generalization performance of SVM regression (with proposed choiceε) with robust regression using 'least-modulus' loss function (ε=0). These comparisons indicate superior generalization performance of SVM regression. © Springer-Verlag Berlin Heidelberg 2002.
CITATION STYLE
Cherkassky, V., & Ma, Y. (2002). Selection of meta-parameters for support vector regression. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2415 LNCS, pp. 687–693). Springer Verlag. https://doi.org/10.1007/3-540-46084-5_112
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