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Neural Processing Letters

, Volume 48, Issue 2, pp 1187–1200 | Cite as

A Novel Least Square Twin Support Vector Regression

  • Zhiqiang Zhang
  • Tongling Lv
  • Hui Wang
  • Liming Liu
  • Junyan TanEmail author
Article

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.

Keywords

LSTSVR TWSVR p-norm Sparsity Feature selection 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  • Zhiqiang Zhang
    • 1
  • Tongling Lv
    • 2
  • Hui Wang
    • 3
  • Liming Liu
    • 4
  • Junyan Tan
    • 2
    Email author
  1. 1.School of Mechanical EngineeringBeijing Institute of TechnologyBeijingChina
  2. 2.College of ScienceChina Agricultural UniversityBeijingChina
  3. 3.College of Mathematical ScienceHarbin Normal UniversityHarbinChina
  4. 4.School of StatisticsCapital University of Economics and BusinessBeijingChina

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