Learning Bayesian network classifiers by maximizing conditional likelihood

By Daniel Grossman, Pedro M. Domingos. International Conference on Machine Learning, 2004

341 citations recorded by the source corpus.

How researchers see this paper

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Structure learning in random fields for heart motion abnormality detection (2008, background): “[9] (D ,D ,N) Greedy DAG Search [10] (D ,D ,N) Exhaustive search [26] (D ,U/D,N) Submod-supermod opt.”
Discriminatively trained Markov model for sequence classification (2005, background): “Grossman and Domingos [10] have described an approach for learning parameters that maximize conditional likelihood function in Bayesian Networks instead of the likelihood function.”
A Novel Bayes Model: Hidden Naive Bayes (2009, methodology): “For class probability estimation, conditional log likelihood (CLL) has been recently used to evaluate the quality of class probability estimation yielded by a classifier [8].”
nFOIL: Integrating Naïve Bayes and FOIL (2005, background): “A similar problem also arises in Bayesian Network structure learning (Grossman & Domingos 2004).”