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Naive Bayes classifier is a simple but useful model. Bayesian network is also a kind of probabilistic graphical model, and the simple one can also have well result. A kind of two-layered Bayesian network is used to predict data in MLSP2008 competition and have got the best result in the 5 submitted valid entries.
hidden Markov models (HMM) are probabilistic graphical models for interdependent classification. In this paper we experiment with different ways of combining the components of an HMM for document analysis applications, in particular for finding tables in text. We show: a) how to integrate different document structure finders into the HMM; b) that transition probabilities should vary along the chain...
In this paper we propose a hybrid probabilistic graphical model for pseudo-likelihood estimation in high-dimensional domains. The model is based on Bayesian networks and Markov random fields. On the one hand, we prove that the proposed model is more expressive than Bayesian networks in terms of the representable distributions. On the other hand, we develop a computationally efficient structure learning...
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