Learning Bayesian Nets that Perform Well
- Russ Greiner, Dept of Computing Science; PI of AICML
- Adam Grove
- Dale Schuurmans, AICML
Bayesian net (BN) is more than a succinct way to encode a probabilistic distribution; it also corresponds to a function used to answer queries. A BN can therefore be evaluated by the accuracy of the answers it returns. Many algorithms for learning BNs, however, attempt to optimize some other criteria (eg, based on Kullback-Leibler divergence, or Bayesian Information Criterion), which is independent of the distribution of queries that are posed. This paper takes the ``performance criteria'' seriously, and considers the challenge of computing the BN whose performance --- read ``accuracy over the distribution of queries'' --- is optimal. We show that many aspects of this learning task are more difficult than the corresponding subtasks in the standard model, and then present an important subclass of queries that greatly simplifies our learning task.
Citation
R. Greiner, A. Grove, D. Schuurmans. "Learning Bayesian Nets that Perform Well". Conference on Uncertainty in Artificial Intelligence (UAI), Providence, Rhode Island, August 1997.| Keywords: | ELR, two samples, discriminative learning, belief nets, learning, machine learning |
| Category: | In Conference |
BibTeX
@incollection{Greiner+al:UAI97,
author = {Russ Greiner and Adam Grove and Dale Schuurmans},
title = {Learning Bayesian Nets that Perform Well},
booktitle = {Conference on Uncertainty in Artificial Intelligence (UAI)},
year = 1997,
}Last Updated: April 24, 2007Submitted by Christian Smith
