On-line learning in neural networks

David Saad (Editor)

Research output: Book/ReportBook

Abstract

On-line learning is one of the most powerful and commonly used techniques for training large layered networks and has been used successfully in many real-world applications. Traditional analytical methods have been recently complemented by ones from statistical physics and Bayesian statistics. This powerful combination of analytical methods provides more insight and deeper understanding of existing algorithms and leads to novel and principled proposals for their improvement. This book presents a coherent picture of the state-of-the-art in the theoretical analysis of on-line learning. An introduction relates the subject to other developments in neural networks and explains the overall picture. Surveys by leading experts in the field combine new and established material and enable non-experts to learn more about the techniques and methods used. This book, the first in the area, provides a comprehensive view of the subject and will be welcomed by mathematicians, scientists and engineers, whether in industry or academia.
Original languageEnglish
Place of PublicationCambridge
PublisherCambridge University Press
Number of pages408
Volume17
ISBN (Print)0262194163
DOIs
Publication statusPublished - Jan 1999

Publication series

NamePublications of the Newton Institute
PublisherCambridge University Press

Keywords

  • On-line learning
  • training large layered networks

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  • A Bayesian approach to on-line learning

    Opper, M. & Winther, O., Jan 1999, On-line learning in neural networks. Saad, D. (ed.). Cambridge: Cambridge University Press, p. 363-378 16 p. (Publications of the Newton Institute; vol. 17).

    Research output: Chapter in Book/Report/Conference proceedingChapter

    Open Access
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