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A flexible support vector machine for regression

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Abstract

In this paper, a novel regression algorithm coined flexible support vector regression is proposed. We first model the insensitive zone in classic support vector regression, respectively, by its up- and down-bound functions and then give a kind of generalized parametric insensitive loss function (GPILF). Subsequently, based on GPILF, we propose an optimization criterion such that the unknown regressor and its up- and down-bound functions can be found simultaneously by solving a single quadratic programming problem. Experimental results on both several publicly available benchmark data sets and time series prediction show the feasibility and effectiveness of the proposed method.

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Acknowledgments

The authors would like to thank the anonymous reviewers for their critical and constructive comments and suggestions. The authors are also thankful to Scientific Foundation of Jiangsu province (BK2010339) and Natural Science Fund for Colleges and Universities in Jiangsu Province (10KJD580001).

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Authors and Affiliations

  1. School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, 210094, People’s Republic of China

    Xiaobo Chen & Jian Yang

  2. School of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang, 212013, People’s Republic of China

    Xiaobo Chen & Jun Liang

Authors
  1. Xiaobo Chen
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  2. Jian Yang
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  3. Jun Liang
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Correspondence to Xiaobo Chen.

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Chen, X., Yang, J. & Liang, J. A flexible support vector machine for regression. Neural Comput & Applic 21, 2005–2013 (2012). https://doi.org/10.1007/s00521-011-0623-5

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  • DOI: https://doi.org/10.1007/s00521-011-0623-5

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