Abstract
Firstly, a heuristic algorithm for labeling the “outlierness” of samples is presented in this paper. Then based on it, a heuristic weight-setting algorithm for least squares support vector machine (LS-SVM) is proposed to obtain the robust estimations. In the proposed algorithm, the weights are set according to the changes of the observed value in the neighborhood of a sample’s input space. Numerical experiments show that the heuristic weight-setting algorithm is able to set appropriate weights on noisy data and hence effectively improves the robustness of LS-SVM.
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Wen, W., Hao, Z., Shao, Z., Yang, X., Chen, M. (2006). A Heuristic Weight-Setting Algorithm for Robust Weighted Least Squares Support Vector Regression. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893028_86
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DOI: https://doi.org/10.1007/11893028_86
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46479-2
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