Maximum Margin Bayesian Network Classifiers
The authors present a maximum margin parameter learning algorithm for Bayesian network classifiers using a Conjugate Gradient (CG) method for optimization. In contrast to previous approaches, they maintain the normalization constraints of the parameters of the Bayesian network during optimization, i.e. the probabilistic interpretation of the model is not lost. This enables to handle missing features in discriminatively optimized Bayesian networks. In experiments, they compare the classification performance of maximum margin parameter learning to conditional likelihood and maximum likelihood learning approaches. Discriminative parameter learning significantly outperforms generative maximum likelihood estimation for naive Bayes and tree augmented naive Bayes structures on all considered data sets.