Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Kernel conditional random fields: representation and clique selection
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian Networks
Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian Networks
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
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Training processes of structured prediction models such as structural SVMs involve frequent computations of the maximum-a-posteriori(MAP) prediction given a parameterized model. For specific output structures such as sequences or trees, MAP estimates can be computed efficiently by dynamic programming algorithms such as the Viterbi algorithm and the CKY parser. However, when the output structures can be arbitrary graphs, exact calculation of the MAP estimate is an NP-complete problem. In this paper, we compare exact inference and approximate inference for labeling graphs. We study the exact junction tree and the approximate loopy belief propagation and sampling algorithms in terms of performance and ressource requirements.