IDEAS home Printed from https://ideas.repec.org/b/wsi/wsbook/8243.html
   My bibliography  Save this book

Case-Based Predictions:An Axiomatic Approach to Prediction, Classification and Statistical Learning

Author

Listed:
  • Itzhak Gilboa

    (Tel-Aviv University, Israel & HEC, Paris, France)

  • David Schmeidler

    (Tel-Aviv University, Israel & Ohio State University, USA)

Abstract

The book presents an axiomatic approach to the problems of prediction, classification, and statistical learning. Using methodologies from axiomatic decision theory, and, in particular, the authors' case-based decision theory, the present studies attempt to ask what inductive conclusions can be derived from existing databases. It is shown that simple consistency rules lead to similarity-weighted aggregation, akin to kernel-based methods. It is suggested that the similarity function be estimated from the data. The incorporation of rule-based reasoning is discussed.

Individual chapters are listed in the "Chapters" tab

Suggested Citation

  • Itzhak Gilboa & David Schmeidler, 2012. "Case-Based Predictions:An Axiomatic Approach to Prediction, Classification and Statistical Learning," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 8243, August.
    • Handle: RePEc:wsi:wsbook:8243
    as

    Download full text from publisher

    File URL: https://www.worldscientific.com/worldscibooks/10.1142/8243
    Download Restriction: Ebook Access is available upon purchase
    ---><---

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

    Other versions of this item:

    Book Chapters

    The following chapters of this book are listed in IDEAS

    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:wsi:wsbook:8243. 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: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscientific.com/page/worldscibooks .

    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.