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2008
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Laplace maximum margin Markov networks

12 years 2 months ago
Laplace maximum margin Markov networks
We propose Laplace max-margin Markov networks (LapM3 N), and a general class of Bayesian M3 N (BM3 N) of which the LapM3 N is a special case with sparse structural bias, for robust structured prediction. BM3 N generalizes extant structured prediction rules based on point estimator to a Bayes-predictor using a learnt distribution of rules. We present a novel Structured Maximum Entropy Discrimination (SMED) formalism for combining Bayesian and max-margin learning of Markov networks for structured prediction, and our approach subsumes the conventional M3 N as a special case. An efficient learning algorithm based on variational inference and standard convex-optimization solvers for M3 N, and a generalization bound are offered. Our method outperforms competing ones on both synthetic and real OCR data.
Jun Zhu, Eric P. Xing, Bo Zhang
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2008
Where ICML
Authors Jun Zhu, Eric P. Xing, Bo Zhang
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