Support vector machines regression with unbounded sampling |
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Authors: | Hongzhi Tong Di-Rong Chen Fenghong Yang |
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Institution: | 1. School of Statistics, University of International Business and Economics , Beijing, P.R. China.tonghz@uibe.edu.cn;3. Department of Mathematics and LMIB, Beijing University of Aeronautics and Astronautics , Beijing, P.R. China.;4. School of Statistics and Mathematics, Central University of Finance and Economics , Beijing, P.R. China. |
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Abstract: | Uniform boundedness of output variables is a standard assumption in most theoretical analysis of regression algorithms. This standard assumption has recently been weaken to a moment hypothesis in least square regression (LSR) setting. Although there has been a large literature on error analysis for LSR under the moment hypothesis, very little is known about the statistical properties of support vector machines regression with unbounded sampling. In this paper, we fill the gap in the literature. Without any restriction on the boundedness of the output sampling, we establish an ad hoc convergence analysis for support vector machines regression under very mild conditions. |
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Keywords: | Support vector machines regression unbounded sampling Hoeffding inequality learning rate |
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