Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Learning structured prediction models: a large margin approach
Learning structured prediction models: a large margin approach
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Accurate max-margin training for structured output spaces
Proceedings of the 25th international conference on Machine learning
Sparse higher order conditional random fields for improved sequence labeling
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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Typical structured learning models consist of a regression component of the explanatory variables (observations) and another regression component that accounts for the neighboring states. Such models, including Conditional Random Fields (CRFs) and Maximum Margin Markov Network (M3N), are essentially Markov random fields with the pairwise spatial dependence. They are effective tools for modeling spatial correlated responses; however, ignoring the temporal correlation often limits their performance to model the more complex scenarios. In this paper, we introduce a novel Temporal Maximum Margin Markov Network (TM3N) model to learn the spatial-temporal correlated hidden states, simultaneously. For learning, we estimate the model's parameters by leveraging on loopy belief propagation (LBP); for predicting, we forecast hidden states use linear integer programming (LIP); for evaluation, we apply TM3N to the simulated datasets and the real world challenge for occupancy estimation. The results are compared with other state-of the art models and demonstrate superior performance.