Abstract
The least squares support vector machine(LS-SVM) is a widely applicable and useful machine learning technique for classification and regression. The solution of LS-SVM is easily obtained from the linear Karush-Kuhn-Tucker conditions instead of a quadratic programming problem of SVM. However, LS-SVM is less robust due to the assumption of the errors and the use of a squared loss function. In this paper we propose a robust LS-SVM regression method which imposes the robustness on the estimation of LS-SVM regression by assigning weight to each data point, which represents the membership degree to cluster. In the numerical studies, the robust LS-SVM regression is compared with the ordinary LS-SVM regression.
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© 2006 Springer-Verlag Berlin Heidelberg
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Shim, J., Hwang, C., Nau, S. (2006). Robust LS-SVM Regression Using Fuzzy C-Means Clustering. In: Jiao, L., Wang, L., Gao, Xb., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881070_26
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DOI: https://doi.org/10.1007/11881070_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45901-9
Online ISBN: 978-3-540-45902-6
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