IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-4-431-56922-0_7.html

Classification

In: Minimum Divergence Methods in Statistical Machine Learning

Author

Listed:
  • Shinto Eguchi

    (Institute of Statistical Mathematic)

  • Osamu Komori

    (Seikei University)

Abstract

This chapter discusses recent developments for pattern recognitionPattern recognitionfocusing on boosting approach in machine learning. The statistical properties such as Bayes risk for several loss functions are discussed in a probabilistic framework. There are a number of loss functions proposed for different purposes and targets. A unified derivation is given by a generator function U which naturally defines entropy, divergence and loss function. The class of U-loss functionsU-loss function associates with the boosting learning algorithms for the loss minimization, which includes AdaBoostAdaBoost and LogitBoost as a twin generated from Kullback-Leibler divergence, and the (partial) area under the ROCReceiver Operating Characteristic (ROC) curve curve(Partial) area under the ROC curve, the.

Suggested Citation

  • Shinto Eguchi & Osamu Komori, 2022. "Classification," Springer Books, in: Minimum Divergence Methods in Statistical Machine Learning, chapter 0, pages 179-195, Springer.
    • Handle: RePEc:spr:sprchp:978-4-431-56922-0_7
    DOI: 10.1007/978-4-431-56922-0_7
    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
    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:sprchp:978-4-431-56922-0_7. 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.