Or gate Bayesian networks for text classification: A discriminative alternative approach to multinom

We propose a simple Bayesian network-based text classifier, which may be considered as a discriminative counterpart of the genera- tive multinomial naive Bayes classifier. The method relies on the use of a fixed net- work topology with the arcs going form term nodes to class nodes, and also on a network parametrization based on noisy or gates. Comparative experiments of the proposed method with naive Bayes and Rocchio algo- rithms are carried out using three standard document collections. p(ci)p(dj |ci) p(dj) ∝ p(ci)p(dj |ci) .