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Proceedings Paper

Research on smoothing support vector regression based on cubic spline interpolation
Author(s): Bin Ren; LiangLun Cheng
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Paper Abstract

Smoothing functions can transform the unsmooth support vector regression into smooth ones,and thus better regression results are generated. In the paper,using Cubic Spline Interpolation, a new polynomial smoothing function is proposed for the [equation] function in ε-insensitive support vector regressions. Theoretical analysis shows that [equation] function is better than [equation]-function in properties, and the approximation accuracy of the proposed smoothing function is two order of magnitude higher than that of the [equation] -function. The experimental results show the validity of the model. Therefore, the new better polynomial smooth functions is provided for smoothing support vector regression and related areas.

Paper Details

Date Published: 19 August 2010
PDF: 8 pages
Proc. SPIE 7820, International Conference on Image Processing and Pattern Recognition in Industrial Engineering, 78201G (19 August 2010); doi: 10.1117/12.866634
Show Author Affiliations
Bin Ren, Guangdong Univ. of Technology (China)
Dongguan Univ. of Technology (China)
LiangLun Cheng, Guangdong Univ. of Technology (China)


Published in SPIE Proceedings Vol. 7820:
International Conference on Image Processing and Pattern Recognition in Industrial Engineering
Shaofei Wu; Zhengyu Du; Shaofei Wu; Zhengyu Du; Shaofei Wu; Zhengyu Du, Editor(s)

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