Maximum Margin Bayesian Network Classifiers
Classification is an important task in machine learning. It deals with assigning a given object to one of a number of different categories. We present a maximum margin parameter learning algorithm for Bayesian network classifiers using a conjugate gradient method for optimization to solve this task. In contrast to previous approaches, we 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. The potentials of the proposed method as well as a comparison to other existing work on maximum margin Bayesian networks is focus of this work.
Franz Pernkopf, Michael Wohlmayr, Sebastian Tschiatschek, “Maximum Margin Bayesian Network Classifiers,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 3, pp. 521-532, 2012.
The provided software may be used free of charge for research purposes. For other uses and further support please contact Franz Pernkopf.