A Convergent Nonlinear Smooth Support Vector Regression Model
作者： Li-ru Tian , Xiao-dan ZHANG
DOI: 10.2991/978-94-6239-102-4_43
关键词: Kernel method 、 Polynomial kernel 、 Smoothing spline 、 Polynomial regression 、 Principal component regression 、 Mathematical optimization 、 Least squares support vector machine 、 Support vector machine 、 Nonlinear regression 、 Mathematics 、 Applied mathematics
摘要: Research on the non-smooth problems in nonlinear support vector regression. A smooth regression model is proposed. Using a generalized cubic spline function approach part model. The of solved by BFGS-Armijo. Then, approximation accuracy and astringency to insensitive loss were analyzed. As result, we found four-order six times function’s effect better than other functions, model, which be proposed this paper convergent.
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