A robust algorithm of support vector regression with a trimmed Huber loss function in the primal
文献类型：期刊论文
| 作者 | Chen, Chuanfa1,2,3; Yan, Changqing4; Zhao, Na5; Guo, Bin3; Liu, Guolin3 |
| 刊名 | SOFT COMPUTING
 |
| 出版日期 | 2017-09-01 |
| 卷号 | 21期号:18页码:5235-5243 |
| 关键词 | Support vector regression Robust Outliers Function estimation |
| ISSN号 | 1432-7643 |
| DOI | 10.1007/s00500-016-2229-4 |
| 通讯作者 | Chen, Chuanfa(chencf@lreis.ac.cn) |
| 英文摘要 | Support vector machine for regression (SVR) is an efficient tool for solving function estimation problem. However, it is sensitive to outliers due to its unbounded loss function. In order to reduce the effect of outliers, we propose a robust SVR with a trimmed Huber loss function (SVRT) in this paper. Synthetic and benchmark datasets were, respectively, employed to comparatively assess the performance of SVRT, and its results were compared with those of SVR, least squares SVR (LS-SVR) and a weighted LS-SVR. The numerical test shows that when training samples are subject to errors with a normal distribution, SVRT is slightly less accurate than SVR and LS-SVR, yet more accurate than the weighted LS-SVR. However, when training samples are contaminated by outliers, SVRT has a better performance than the other methods. Furthermore, SVRT is faster than the weighted LS-SVR. Simulating eight benchmark datasets shows that SVRT is averagely more accurate than the other methods when sample points are contaminated by outliers. In conclusion, SVRT can be considered as an alternative robust method for simulating contaminated sample points. |
| WOS关键词 | OUTLIERS ; MACHINE ; NOISE ; APPROXIMATION ; TUTORIAL ; NETWORKS |
| 资助项目 | National Natural Science Foundation of China[41371367] ; National Natural Science Foundation of China[41101433] ; SDUST Research Fund ; Joint Innovative Center for Safe And Effective-Mining Technology and Equipment of Coal Resources, Shandong Province ; Special Project Fund of Taishan Scholars of Shandong Province |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000410259700006 |
| 出版者 | SPRINGER |
| 资助机构 | National Natural Science Foundation of China ; SDUST Research Fund ; Joint Innovative Center for Safe And Effective-Mining Technology and Equipment of Coal Resources, Shandong Province ; Special Project Fund of Taishan Scholars of Shandong Province |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/61947]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Chen, Chuanfa |
| 作者单位 | 1.Shandong Univ Sci & Technol, State Key Lab Min Disaster Prevent & Control Co f, Qingdao 266590, Peoples R China 2.Shandong Univ Sci & Technol, Minist Sci & Technol, Qingdao 266590, Peoples R China 3.Shandong Univ Sci & Technol, Coll Geomat, Qingdao 266590, Peoples R China 4.Shandong Univ Sci & Technol, Dept Informat Engn, Tai An 271019, Shandong, Peoples R China 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Chuanfa,Yan, Changqing,Zhao, Na,et al. A robust algorithm of support vector regression with a trimmed Huber loss function in the primal[J]. SOFT COMPUTING,2017,21(18):5235-5243. |
| APA | Chen, Chuanfa,Yan, Changqing,Zhao, Na,Guo, Bin,&Liu, Guolin.(2017).A robust algorithm of support vector regression with a trimmed Huber loss function in the primal.SOFT COMPUTING,21(18),5235-5243. |
| MLA | Chen, Chuanfa,et al."A robust algorithm of support vector regression with a trimmed Huber loss function in the primal".SOFT COMPUTING 21.18(2017):5235-5243. |
入库方式： OAI收割
来源：地理科学与资源研究所
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