Opper, Manfred and Winther, Ole (1999). A Bayesian approach to on-line learning. IN: On-line learning in neural networks. Saad, David (ed.) Publications of the Newton Institute . Cambridge: Cambridge University Press.
Abstract
Online learning is discussed from the viewpoint of Bayesian statistical inference. By replacing the true posterior distribution with a simpler parametric distribution, one can define an online algorithm by a repetition of two steps: An update of the approximate posterior, when a new example arrives, and an optimal projection into the parametric family. Choosing this family to be Gaussian, we show that the algorithm achieves asymptotic efficiency. An application to learning in single layer neural networks is given.
Publication DOI: | https://doi.org/10.2277/0521652634 |
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Divisions: | College of Engineering & Physical Sciences > Systems analytics research institute (SARI) |
Additional Information: | Copyright of Cambridge University Press Available on Google Books |
Uncontrolled Keywords: | Online learning,Bayesian statistical inference,asymptotic efficiency,neural networks |
ISBN: | 0262194163 |
Last Modified: | 05 Aug 2024 08:55 |
Date Deposited: | 05 Aug 2009 13:32 |
Full Text Link: | |
Related URLs: |
http://www.camb ... isbn=0521652634
(Publisher URL) |
PURE Output Type: | Chapter |
Published Date: | 1999-01 |
Authors: |
Opper, Manfred
Winther, Ole |