IDEAS home Printed from https://ideas.repec.org/a/spr/comgts/v6y2009i1p53-79.html
   My bibliography  Save this article

The weight-decay technique in learning from data: an optimization point of view

Author

Listed:
  • Giorgio Gnecco
  • Marcello Sanguineti

Abstract

No abstract is available for this item.

Suggested Citation

  • Giorgio Gnecco & Marcello Sanguineti, 2009. "The weight-decay technique in learning from data: an optimization point of view," Computational Management Science, Springer, vol. 6(1), pages 53-79, February.
    • Handle: RePEc:spr:comgts:v:6:y:2009:i:1:p:53-79
    DOI: 10.1007/s10287-008-0072-5
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10287-008-0072-5
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10287-008-0072-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

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

    References listed on IDEAS

    as
    1. Amit Gupta & Monica Lam, 1998. "The weight decay backpropagation for generalizations with missing values," Annals of Operations Research, Springer, vol. 78(0), pages 165-187, January.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Hong Seok Park & Dinh Son Nguyen & Thai Le-Hong & Xuan Tran, 2022. "Machine learning-based optimization of process parameters in selective laser melting for biomedical applications," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1843-1858, August.
    2. L.-F. Pau, 2014. "Discovering the dynamics of smart business networks," Computational Management Science, Springer, vol. 11(4), pages 445-458, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Souad Larabi-Marie-Sainte & Roohi Jan & Ali Al-Matouq & Sara Alabduhadi, 2021. "The impact of timetable on student’s absences and performance," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-22, June.
    2. Bildirici, Melike & Ersin, Özgür, 2012. "Nonlinear volatility models in economics: smooth transition and neural network augmented GARCH, APGARCH, FIGARCH and FIAPGARCH models," MPRA Paper 40330, University Library of Munich, Germany, revised May 2012.

    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:comgts:v:6:y:2009:i:1:p:53-79. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.