Abstract
This paper presents the ν-SVM and the ν-SVR full regularization paths along with a leave-one-out inspired stopping criterion and an efficient implementation. In the ν-SVR method, two parameters are provided by the user: the regularization parameter C and ν which settles the width of the ε-tube. In the classical ν-SVM method, parameter ν is an lower bound on the number of support vectors in the solution. Based on the previous works of [1,2], extensions of regularization paths for SVM and SVR are proposed and permit to automatically compute the solution path by varying ν or the regularization parameter.
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Loosli, G., Gasso, G., Canu, S. (2007). Regularization Paths for ν-SVM and ν-SVR. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_62
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DOI: https://doi.org/10.1007/978-3-540-72395-0_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72394-3
Online ISBN: 978-3-540-72395-0
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