Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Conditional Random Fields for Contextual Human Motion Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Constraint partitioning in penalty formulations for solving temporal planning problems
Artificial Intelligence
Discriminative language modeling with conditional random fields and the perceptron algorithm
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
CRF-OPT: an efficient high-quality conditional random field solver
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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Conditional random fields(CRFs) are a class of undirected graphical models which have been widely used for classifying and labeling sequence data. The training of CRFs is typically formulated as an unconstrained optimization problem that maximizes the conditional likelihood. However, maximum likelihood training is prone to overfitting. To address this issue, we propose a novel constrained nonlinear optimization formulation in which the prediction accuracy of cross-validation sets are included as constraints. Instead of requiring multiple passes of training, the constrained formulation allows the cross-validation be handled in one pass of constrained optimization. The new formulation is discontinuous, and classical Lagrangian based constraint handling methods are not applicable. A new constrained optimization algorithm based on the recently proposed extended saddle point theory is developed to learn the constrained CRF model. Experimental results on gene and stock-price prediction tasks show that the constrained formulation is able to significantly improve the generalization ability of CRF training.