We propose a simple and efficient approach to building undirected probabilistic classification models (Markov networks) that extend the naive Bayes classifiers and outperform the existing directed probabilistic classifiers of similar complexity (e.g. Bayesian network with same cluster size). The models are represented as sets of cliques, not necessarily maximal, and the probability density functions can be estimated in closed form that mirrors the cluster variation method (Kikuchi approximation). We employ a highly efficient Bayesian learning algorithm, based on integrating along a hill-climb in the structure space. We present promising empirical results on 46 benchmarks.
By: Aleks Jakulin; Irina Rish
Published in: RC23602 in 2005
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