Boosted Bayesian network classifiers

@article{Jing2008BoostedBN,
  title={Boosted Bayesian network classifiers},
  author={Yushi Jing and Vladimir Pavlovic and James M. Rehg},
  journal={Machine Learning},
  year={2008},
  volume={73},
  pages={155-184}
}
The use of Bayesian networks for classification problems has received a significant amount of recent attention. Although computationally efficient, the standard maximum likelihood learning method tends to be suboptimal due to the mismatch between its optimization criteria (data likelihood) and the actual goal of classification (label prediction accuracy). Recent approaches to optimizing classification performance during parameter or structure learning show promise, but lack the favorable… CONTINUE READING

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