Journal of Mechanical Science and Technology

, Volume 29, Issue 1, pp 279–289 | Cite as

Robust estimation of support vector regression via residual bootstrap adoption

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Abstract

As current system designs grow increasingly complex and expensive to analyze, the need for design optimization has also grown. In this study, a more stable approximation model is proposed via the application of a bootstrap to support vector regression (SVR). SVR expresses the nonlinearity of the system relatively well. However, using SVR does not always guarantee accurate results because it is sensitive to the input parameters. To overcome this drawback, we apply a bootstrap to SVR, using the residual from SVR as the bootstrap. The performance of the proposed method is evaluated via application to numerical examples and a real problem. We observed that the proposed method not only produced valuable results but also noticeably eliminated the negative effects of input parameters.

Keywords

Support vector regression Bootstrap Residual Root median square error 

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Copyright information

© The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Won-Young Choi
    • 1
  • Dong-Hoon Choi
    • 2
  • Kyung-Joon Cha
    • 3
  1. 1.College of General EducationHanbat National UniversityDaejeonKorea
  2. 2.Department of Mechanical EngineeringHanyang UniversitySeoulKorea
  3. 3.Department of Mathematics and Research Institute for Natural SciencesHanyang UniversitySeoulKorea

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