Abstract
Research on the non-smooth problems in the nonlinear support vector regression. A nonlinear smooth support vector regression model is proposed. Using a generalized cubic spline function approach the non-smooth part in the support vector regression model. The model of the nonlinear smooth support vector regression is solved by BFGS-Armijo. Then, the approximation accuracy and the astringency of the generalized cubic spline function to the insensitive loss function were analyzed. As a result, we found the four-order and six times spline function’s approximation effect is better than other smooth functions, and the nonlinear smooth support vector regression model, which be proposed in this paper is convergent.
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Tian, Lr., ZHANG, Xd. (2015). A Convergent Nonlinear Smooth Support Vector Regression Model. In: Qi, E., Shen, J., Dou, R. (eds) Proceedings of the 21st International Conference on Industrial Engineering and Engineering Management 2014. Proceedings of the International Conference on Industrial Engineering and Engineering Management. Atlantis Press, Paris. https://doi.org/10.2991/978-94-6239-102-4_43
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DOI: https://doi.org/10.2991/978-94-6239-102-4_43
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