Online Bounds for Bayesian Algorithms. Kakade, S M & Ng, A Y NIPS, 2004. abstract bibtex Abstract We present a competitive analysis of Bayesian learning algorithms in the online learning setting and show that many simple Bayesian algorithms (such as Gaussian linear regression and Bayesian logistic regression) perform favorably when compared, in.
@Article{Kakade2004,
author = {Kakade, S M and Ng, A Y},
title = {Online Bounds for Bayesian Algorithms.},
journal = {NIPS},
volume = {},
number = {},
pages = {},
year = {2004},
abstract = {Abstract We present a competitive analysis of Bayesian learning algorithms in the online learning setting and show that many simple Bayesian algorithms (such as Gaussian linear regression and Bayesian logistic regression) perform favorably when compared, in.},
location = {},
keywords = {}}
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