Research on smoothing support vector regression based on cubic spline interpolation

Bin Ren; LiangLun Cheng

doi:10.1117/12.866634

19 August 2010 Research on smoothing support vector regression based on cubic spline interpolation

Bin Ren, LiangLun Cheng

Proceedings Volume 7820, International Conference on Image Processing and Pattern Recognition in Industrial Engineering; 78201G (2010) https://doi.org/10.1117/12.866634
Event: International Conference on Image Processing and Pattern Recognition in Industrial Engineering, 2010, Xi'an, China

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.

Citation Download Citation

Bin Ren and LiangLun Cheng "Research on smoothing support vector regression based on cubic spline interpolation", Proc. SPIE 7820, International Conference on Image Processing and Pattern Recognition in Industrial Engineering, 78201G (19 August 2010); https://doi.org/10.1117/12.866634

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