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A Novel Least Square Twin Support Vector Regression

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Abstract

This paper proposes a new method for regression named lp norm least square twin support vector regression (PLSTSVR), which is formulated by the idea of twin support vector regression (TSVR). Different from TSVR, our new model is an adaptive learning procedure with p-norm SVM (\({{0<p\le 2}}\)), where p is viewed as an adjustable parameter and can be automatically chosen by data. An iterative algorithm is suggested to solve PLSTSVR efficiently. In each iteration, only a series systems of linear equations (LEs) are solved. Experiments carried out on several standard UCI datasets and synthetic datasets show the feasibility and effectiveness of the proposed method.

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Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China

    Zhiqiang Zhang

  2. College of Science, China Agricultural University, Beijing, 100083, China

    Tongling Lv & Junyan Tan

  3. College of Mathematical Science, Harbin Normal University, Harbin, 150080, China

    Hui Wang

  4. School of Statistics, Capital University of Economics and Business, Beijing, 100081, China

    Liming Liu

Authors
  1. Zhiqiang Zhang
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  2. Tongling Lv
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  3. Hui Wang
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  4. Liming Liu
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  5. Junyan Tan
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Corresponding author

Correspondence to Junyan Tan.

Additional information

Tongling Lv: Co-first author.

This paper is supported by the National Natural Science Foundation of China (Grant No. 11301535) and Chinese University Scientific Fund (No. 2017LX003).

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Cite this article

Zhang, Z., Lv, T., Wang, H. et al. A Novel Least Square Twin Support Vector Regression. Neural Process Lett 48, 1187–1200 (2018). https://doi.org/10.1007/s11063-017-9773-5

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  • DOI: https://doi.org/10.1007/s11063-017-9773-5

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