| Autor: |
King, Irwin, Jun Wang, Laiwan Chan, DeLiang Wang, Wen Wen, Zhifeng Hao, Zhuangfeng Shao, Xiaowei Yang, Ming Chen |
| Zdroj: |
Neural Information Processing; 2006, p773-781, 9p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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