IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-642-32054-5_193.html
   My bibliography  Save this book chapter

The Arc Learning Algorithm Based on Extended Functional Dependency

In: Liss 2012

Author

Listed:
  • Ying Qu

    (Hebei University of Science and Technology)

  • Jingru Wu

    (Hebei University of Science and Technology)

  • Shuai Li

    (Hebei University of Science)

  • Yanan Wang

    (Hebei University of Science and Technology)

Abstract

Taking the consideration about that the existing structure learning methods for Credal Network Structure separate the relation between arc’s existence and direction and require sample data strictly, the concept of extended functional dependency is put forward to solve the above problem; Make full use of extended functional dependency to express the existence and direction of the arc, so it makes the two processes united. Furthermore, the concept of dependency degree is brought forward to make the requirement for data set not so strictly; thus, the available of the network structure in the context of incomplete data set is ensured. Transfer the question of structure learning into the question of extended functional dependency discovering. The algorithm for discovering extended functional dependency is designed, and the accuracy of the method is proved with examples.

Suggested Citation

  • Ying Qu & Jingru Wu & Shuai Li & Yanan Wang, 2013. "The Arc Learning Algorithm Based on Extended Functional Dependency," Springer Books, in: Zhenji Zhang & Runtong Zhang & Juliang Zhang (ed.), Liss 2012, edition 127, pages 1361-1366, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-32054-5_193
    DOI: 10.1007/978-3-642-32054-5_193
    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.

    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:sprchp:978-3-642-32054-5_193. 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.