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Incremental Bayesian networks for structure prediction
2007
Proceedings of the 24th international conference on Machine learning - ICML '07
We propose a class of graphical models appropriate for structure prediction problems where the model structure is a function of the output structure. Incremental Sigmoid Belief Networks (ISBNs) avoid the need to sum over the possible model structures by using directed arcs and incrementally specifying the model structure. Exact inference in such directed models is not tractable, but we derive two efficient approximations based on mean field methods, which prove effective in artificial
doi:10.1145/1273496.1273608
dblp:conf/icml/TitovH07
fatcat:bvqxmwsl5rafbaylaob2y7f2ua