@inproceedings{2230203f6dae41ed851debca3d94b769,
title = "Selection of meta-parameters for support vector regression",
abstract = "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.",
author = "Vladimir Cherkassky and Yunqian Ma",
note = "Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 2002 International Conference on Artificial Neural Networks, ICANN 2002 ; Conference date: 28-08-2002 Through 30-08-2002",
year = "2002",
doi = "10.1007/3-540-46084-5_112",
language = "English (US)",
isbn = "9783540440741",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "687--693",
editor = "Dorronsoro, {Jose R.} and Dorronsoro, {Jose R.}",
booktitle = "Artificial Neural Networks, ICANN 2002 - International Conference, Proceedings",
}