Adaptive Online Learning of Bayesian Network Parameters (2001)
| Citations: | 7 - 2 self |
BibTeX
@MISC{Bronstein01adaptiveonline,
author = {Re Bronstein and Ira Cohen and Ira Cohen and Fabio G. Cozman and Fabio G. Cozman and Escola Politecnica and Alexandre Bronstein Fabio},
title = {Adaptive Online Learning of Bayesian Network Parameters},
year = {2001}
}
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Abstract
The paper introduces Voting EM, an adaptive online learning algorithm of Bayesian network parameters. Voting EM is an extension of the EM( ) algorithm suggested by [1]. We show convergence properties of the Voting EM that uses a constant learning rate. We use the convergence properties to formulate an error driven scheme for adapting the learning rate. The resultant algorithm converges with the optimal rate of near a maximum while retaining the ability to increase the learning rate in the vicinity of a local maximum or due to changes in the modelled environment.
