IDEAS home Printed from https://ideas.repec.org/h/spr/oprchp/978-3-319-28697-6_35.html
   My bibliography  Save this book chapter

Row and Column Generation Algorithm for Maximization of Minimum Margin for Ranking Problems

In: Operations Research Proceedings 2014

Author

Listed:
  • Yoichi Izunaga

    (University of Tsukuba)

  • Keisuke Sato

    (Railway Technical Research Institute)

  • Keiji Tatsumi

    (Osaka University)

  • Yoshitsugu Yamamoto

    (University of Tsukuba)

Abstract

We consider the ranking problem of learning a ranking function from the data set of objects each of which is endowed with an attribute vector and a ranking label chosen from the ordered set of labels. We propose two different formulations: primal problem, primal problem with dual representation of normal vector, and then propose to apply the kernel technique to the latter formulation. We also propose algorithms based on the row and column generation in order to mitigate the computational burden due to the large number of objects.

Suggested Citation

  • Yoichi Izunaga & Keisuke Sato & Keiji Tatsumi & Yoshitsugu Yamamoto, 2016. "Row and Column Generation Algorithm for Maximization of Minimum Margin for Ranking Problems," Operations Research Proceedings, in: Marco Lübbecke & Arie Koster & Peter Letmathe & Reinhard Madlener & Britta Peis & Grit Walther (ed.), Operations Research Proceedings 2014, edition 1, pages 249-255, Springer.
    • Handle: RePEc:spr:oprchp:978-3-319-28697-6_35
    DOI: 10.1007/978-3-319-28697-6_35
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:oprchp:978-3-319-28697-6_35. 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.springer.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.