IDEAS home Printed from
   My bibliography  Save this article

Knowledge based proximal support vector machines


  • Khemchandani, Reshma
  • Jayadeva
  • Chandra, Suresh


We propose a proximal version of the knowledge based support vector machine formulation, termed as knowledge based proximal support vector machines (KBPSVMs) in the sequel, for binary data classification. The KBPSVM classifier incorporates prior knowledge in the form of multiple polyhedral sets, and determines two parallel planes that are kept as distant from each other as possible. The proposed algorithm is simple and fast as no quadratic programming solver needs to be employed. Effectively, only the solution of a structured system of linear equations is needed.

Suggested Citation

  • Khemchandani, Reshma & Jayadeva & Chandra, Suresh, 2009. "Knowledge based proximal support vector machines," European Journal of Operational Research, Elsevier, vol. 195(3), pages 914-923, June.
  • Handle: RePEc:eee:ejores:v:195:y:2009:i:3:p:914-923

    Download full text from publisher

    File URL:
    Download Restriction: Full text for ScienceDirect subscribers only

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


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

    Cited by:

    1. Khemchandani, Reshma & Jayadeva & Chandra, Suresh, 2010. "Learning the optimal kernel for Fisher discriminant analysis via second order cone programming," European Journal of Operational Research, Elsevier, vol. 203(3), pages 692-697, June.
    2. Kaiquan Xu & Stephen Shaoyi Liao & Raymond Y. K. Lau & J. Leon Zhao, 2014. "Effective Active Learning Strategies for the Use of Large-Margin Classifiers in Semantic Annotation: An Optimal Parameter Discovery Perspective," INFORMS Journal on Computing, INFORMS, vol. 26(3), pages 461-483, August.
    3. Aytug, Haldun & Sayın, Serpil, 2012. "Exploring the trade-off between generalization and empirical errors in a one-norm SVM," European Journal of Operational Research, Elsevier, vol. 218(3), pages 667-675.
    4. Cassioli, A. & Chiavaioli, A. & Manes, C. & Sciandrone, M., 2013. "An incremental least squares algorithm for large scale linear classification," European Journal of Operational Research, Elsevier, vol. 224(3), pages 560-565.


    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:eee:ejores:v:195:y:2009:i:3:p:914-923. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.