{37 () Bayesian Network Classiiers *

@inproceedings{Friedman199737B,
  title={\{37 () Bayesian Network Classiiers *},
  author={Nir Friedman and Dan Geiger},
  year={1997}
}
Recent work in supervised learning has shown that a surprisingly simple Bayesian classiier with strong assumptions of independence among features, called naive Bayes, is competitive with state-of-the-art classiiers such as C4.5. This fact raises the question of whether a classiier with less restrictive assumptions can perform even better. In this paper we evaluate approaches for inducing classiiers from data, based on the theory of learning Bayesian networks. These networks are factored… CONTINUE READING
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