Two smooth support vector machines for ε -insensitive regression

Weizhe Gu, Wei Po Chen, Chun Hsu Ko, Yuh Jye Lee, Jein Shan Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


In this paper, we propose two new smooth support vector machines for ε-insensitive regression. According to these two smooth support vector machines, we construct two systems of smooth equations based on two novel families of smoothing functions, from which we seek the solution to ε-support vector regression (ε-SVR). More specifically, using the proposed smoothing functions, we employ the smoothing Newton method to solve the systems of smooth equations. The algorithm is shown to be globally and quadratically convergent without any additional conditions. Numerical comparisons among different values of parameter are also reported.

Original languageEnglish
Pages (from-to)171-199
Number of pages29
JournalComputational Optimization and Applications
Issue number1
Publication statusPublished - 2018 May 1


  • Smoothing Newton algorithm
  • Support vector machine
  • ε-insensitive loss
  • ε-smooth support vector regression

ASJC Scopus subject areas

  • Control and Optimization
  • Computational Mathematics
  • Applied Mathematics


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