Laplace maximum margin Markov networks

Authors:
Jun Zhu;Eric P. Xing;Bo Zhang
Affiliations:
Carnegie Mellon University, Pittsburgh, PA and Tsinghua University, Beijing, China;Carnegie Mellon University, Pittsburgh, PA;Tsinghua University, Beijing, China
Venue:
Proceedings of the 25th international conference on Machine learning
Year:
2008

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Abstract

We propose Laplace max-margin Markov networks (LapM3N), and a general class of Bayesian M3N (BM3N) of which the LapM3N is a special case with sparse structural bias, for robust structured prediction. BM3N 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 M3N as a special case. An efficient learning algorithm based on variational inference and standard convex-optimization solvers for M3N, and a generalization bound are offered. Our method outperforms competing ones on both synthetic and real OCR data.