Estimation of the regularization parameter for support vectorregression
Jordaan, E.M.
Smits, G.F.
Dept. of Math. & Comput. Sci., Eindhoven Univ. of Technol.;
This paper appears in: Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Publication Date: 2002
Volume: 3,
On page(s): 2192-2197
Meeting Date: 05/12/2002 - 05/17/2002
Location: Honolulu, HI, USA
ISBN: 0-7803-7278-6
References Cited: 8
INSPEC Accession Number: 7328594
Digital Object Identifier: 10.1109/IJCNN.2002.1007481
Current Version Published: 2002-08-07
Abstract
Support vector machines use a regularization parameter C to
regulate the trade-off between the complexity of the model and the
empirical risk of the model. Most of the techniques available for
determining the optimal value of C are very time consuming. For
industrial applications of the SVM method, there is a need for a fast
and robust method to estimate C. A method based on the characteristics
of the kernel, the range of output values and the size of the
ε-insensitive zone, is proposed
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