IDEAS home Printed from https://ideas.repec.org/a/pal/jorsoc/v64y2013i5p775-785.html
   My bibliography  Save this article

Multiphase support vector regression for function approximation with break-points

Author

Listed:
  • J I Park

    (The State University of New Jersey, Piscataway, USA)

  • N Kim

    (The State University of New Jersey, Piscataway, USA)

  • M K Jeong

    (The State University of New Jersey, Piscataway, USA)

  • K S Shin

    (The State University of New Jersey, Piscataway, USA)

Abstract

In this paper, we propose a novel multiphase support vector regression (mp-SVR) technique to approximate a true relationship for the case where the effect of input on output changes abruptly at some break-points. A new formulation for mp-SVR is presented to allow such structural changes in regression function. And then, we present a new hybrid-encoding scheme in genetic algorithms to select the best combination of the kernel functions and to determine both break-points and hyperparameters of mp-SVR. The proposed method has a major advantage over the conventional ones that different kernel functions can be possibly adapted to different regions of the data domain. Computational results in two examples including a real-life data demonstrate its capability in capturing the local characteristics of the data more effectively. Consequently, the mp-SVR has a high potential value in a wide range of applications for function approximations.

Suggested Citation

  • J I Park & N Kim & M K Jeong & K S Shin, 2013. "Multiphase support vector regression for function approximation with break-points," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(5), pages 775-785, May.
    • Handle: RePEc:pal:jorsoc:v:64:y:2013:i:5:p:775-785
    as

    Download full text from publisher

    File URL: http://www.palgrave-journals.com/jors/journal/v64/n5/pdf/jors201241a.pdf
    File Function: Link to full text PDF
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: http://www.palgrave-journals.com/jors/journal/v64/n5/full/jors201241a.html
    File Function: Link to full text HTML
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gianluca Gazzola & Myong K. Jeong, 2021. "Support vector regression for polyhedral and missing data," Annals of Operations Research, Springer, vol. 303(1), pages 483-506, August.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:jorsoc:v:64:y:2013:i:5:p:775-785. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave-journals.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.