| Ren Bin (任斌),He Chunhong,Liu Huijie,Yang Lei,Xie Guobo.[J].高技术通讯(英文),2014,20(2):187~194 |
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| A polynomial smooth epsilon-support vector regression based on cubic spline interpolation |
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| DOI：10.3772/j.issn.1006-6748.2014.02.012 |
| 中文关键词: |
| 英文关键词: support vector regression, ε-insensitive loss function, smooth, polynomial function, cubic spline interpolation |
| 基金项目: |
| Author Name | Affiliation | | Ren Bin (任斌) | | | He Chunhong | | | Liu Huijie | | | Yang Lei | | | Xie Guobo | |
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| 中文摘要: |
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| 英文摘要: |
| Regression analysis is often formulated as an optimization problem with squared loss functions. Facing the challenge of the selection of the proper function class with polynomial smooth techniques applied to support vector regression models, this study takes cubic spline interpolation to generate a new polynomial smooth function |x|2ε in ε-insensitive support vector regression. Theoretical analysis shows that S2ε-function is better than p2ε-function in properties, and the approximation accuracy of the proposed smoothing function is two order higher than that of classical p2ε-function. The experimental data shows the efficiency of the new approach. |
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