Model Selection Criteria for Learning Belief Nets: An Empirical Comparison
- Tim Van Allen
- Russ Greiner, Dept of Computing Science; PI of AICML
We are interested in the problem of learning the dependency structure of a belief net, which involves a trade-off between simplicity and goodness of fit to the training data. We describe the results of an empirical comparison of three standard model selection criteria --- viz., a Minimum Description Length criterion (MDL), Akaike's Information Criterion (AIC) and a Cross-Validation criterion --- applied to this problem. Our results suggest that AIC and cross-validation are both good criteria for avoiding overfitting, but MDL does not work well in this context.
Citation
T. Van Allen, R. Greiner. "Model Selection Criteria for Learning Belief Nets: An Empirical Comparison". International Conference on Machine Learning (ICML), Sydney Australia, July 2002.| Keywords: | |
| Category: | In Conference |
BibTeX
@incollection{VanAllen+Greiner:ICML02,
author = {Tim Van Allen and Russ Greiner},
title = {Model Selection Criteria for Learning Belief Nets: An Empirical
Comparison},
booktitle = {International Conference on Machine Learning (ICML)},
year = 2002,
}Last Updated: March 06, 2020Submitted by Sabina P
