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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chih-Ching Hsiao Shun-Feng Su Chen-Chia Chuang |
| Copyright Year | 2011 |
| Description | Author affiliation: Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei City, Taiwan, R.O.C (Shun-Feng Su) || Department of Electrical Engineering, National Ilan University, I-Lan County, Taiwan, ROC (Chen-Chia Chuang) || Department of Electrical Engineering, Kao Yuan University, Kaohsiung City, Taiwan, R.O.C (Chih-Ching Hsiao) |
| Abstract | Support vector regression (SVR) employs the support vector machine (SVM) to tackle problems of function approximation and regression estimation. SVR has been shown to have good robust properties against noise. Besides, in SVR, outliers may also possibly be taken as support vectors. Such an inclusion of outliers in support vectors may lead to seriously overfitting phenomena. The rough set theory is successes to deal with imprecise, incomplete or uncertain for information system. In this paper, a novel regression approach, termed as the Rough Margin Support Vector Regression (RMSVR) network, is proposed to enhance the robust capability of SVR. The basic idea of the approach is to adopt the concept of rough sets to construct the model obtained by SVR and fine tune it with a robust learning algorithm. Simulation results of the proposed approach have shown the effectiveness of the approximated function in discriminating against outliers. |
| Starting Page | 2814 |
| Ending Page | 2818 |
| File Size | 391844 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424473151 |
| ISSN | 10987584 |
| e-ISBN | 9781424473175 |
| e-ISBN | 9781424473168 |
| DOI | 10.1109/FUZZY.2011.6007454 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-06-27 |
| Publisher Place | Taiwan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Support vector machines Robustness Function approximation Rough sets Kernel Least squares approximation robust learning support vector regression(SVR) outlier rough sets |
| Content Type | Text |
| Resource Type | Article |
| Subject | Applied Mathematics Artificial Intelligence Theoretical Computer Science Software |
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