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Large Margin Boltzmann Machines and Large Margin Sigmoid Belief Networks
[article]
2010
arXiv
pre-print
Current statistical models for structured prediction make simplifying assumptions about the underlying output graph structure, such as assuming a low-order Markov chain, because exact inference becomes intractable as the tree-width of the underlying graph increases. Approximate inference algorithms, on the other hand, force one to trade off representational power with computational efficiency. In this paper, we propose two new types of probabilistic graphical models, large margin Boltzmann
arXiv:1003.4781v1
fatcat:uwhwzd4jijasbk6duxyqzmvnre