A Bayesian approach to on-line learning

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.


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
Divisions: Engineering & Applied Sciences > Mathematics
Engineering & Applied 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
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Related URLs: http://www.camb ... isbn=0521652634 (Publisher URL)
Published Date: 1999-01
Authors: Opper, Manfred
Winther, Ole



Version: Accepted Version

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