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Stacked Graphical Models for Efficient Inference in Markov Random Fields
Proceedings of the 2007 SIAM International Conference on Data Mining
In collective classification, classes are predicted simultaneously for a group of related instances, rather than predicting a class for each instance separately. Collective classification has been widely used for classification on relational datasets. However, the inference procedure used in collective classification usually requires many iterations and thus is expensive. We propose stacked graphical learning, a meta-learning scheme in which a base learner is augmented by expanding onedoi:10.1137/1.9781611972771.57 dblp:conf/sdm/KouC07 fatcat:irvkv5lrxbdx3bloimexw72vfe