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Model Selection Criteria for Learning Belief Nets: An Empirical Comparison

Full Text:  PS

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, June 2002.

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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: February 06, 2020
Submitted by Sabina P

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