A quasi-Bayes unsupervised learning procedure for priors (Corresp.)

Authors:
U. Makov;A. Smith
Affiliations:
-;-
Venue:
IEEE Transactions on Information Theory
Year:
2006

Citing 0
Cited 1

On-line EM Variants for Multivariate Normal Mixture Model in Background Learning and Moving Foreground Detection

Journal of Mathematical Imaging and Vision

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Abstract

Unsupervised Bayes sequential learning procedures for classification and estimation are often useless in practice because of the amount of computation required. In this paper, a version of a two-class decision problem is considered, and a quasi-Bayes procedure is motivated and defined. The proposed procedure mimics closely the formal Bayes solution while involving only a minimal amount of computation. Convergence properties are established and some numerical illustrations provided. The approach compares favorably with other non-Bayesian learning procedures that have been proposed and can be extended to more general situations.