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ACL
2006
14 years 6 days ago
Training Conditional Random Fields with Multivariate Evaluation Measures
This paper proposes a framework for training Conditional Random Fields (CRFs) to optimize multivariate evaluation measures, including non-linear measures such as F-score. Our prop...
Jun Suzuki, Erik McDermott, Hideki Isozaki
AAAI
2008
14 years 1 months ago
CRF-OPT: An Efficient High-Quality Conditional Random Field Solver
Conditional random field (CRF) is a popular graphical model for sequence labeling. The flexibility of CRF poses significant computational challenges for training. Using existing o...
Minmin Chen, Yixin Chen, Michael R. Brent
MICCAI
2004
Springer
14 years 11 months ago
3D Bayesian Regularization of Diffusion Tensor MRI Using Multivariate Gaussian Markov Random Fields
3D Bayesian regularization applied to diffusion tensor MRI is presented here. The approach uses Markov Random Field ideas and is based upon the definition of a 3D neighborhood syst...
Marcos Martín-Fernández, Carl-Fredri...
NAACL
2003
14 years 5 days ago
Shallow Parsing with Conditional Random Fields
Conditional random fields for sequence labeling offer advantages over both generative models like HMMs and classifiers applied at each sequence position. Among sequence labeling...
Fei Sha, Fernando C. N. Pereira
NIPS
2004
14 years 5 days ago
Semi-Markov Conditional Random Fields for Information Extraction
We describe semi-Markov conditional random fields (semi-CRFs), a conditionally trained version of semi-Markov chains. Intuitively, a semiCRF on an input sequence x outputs a "...
Sunita Sarawagi, William W. Cohen