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Constrained Approximate Maximum Entropy Learning of Markov Random Fields
[article]
2012
arXiv
pre-print
Parameter estimation in Markov random fields (MRFs) is a difficult task, in which inference over the network is run in the inner loop of a gradient descent procedure. Replacing exact inference with approximate methods such as loopy belief propagation (LBP) can suffer from poor convergence. In this paper, we provide a different approach for combining MRF learning and Bethe approximation. We consider the dual of maximum likelihood Markov network learning - maximizing entropy with moment matching
arXiv:1206.3257v1
fatcat:vhnu5ultb5dcracyyylbzahphe