Corpus ID: 10840

Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction

@article{Bach2013HingelossMR,
  title={Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction},
  author={Stephen H. Bach and Bert Huang and Ben London and L. Getoor},
  journal={ArXiv},
  year={2013},
  volume={abs/1309.6813}
}

Stephen H. Bach, Bert Huang, +1 author L. Getoor

Published 2013

Computer Science, Mathematics

ArXiv

Graphical models for structured domains are powerful tools, but the computational complexities of combinatorial prediction spaces can force restrictions on models, or require approximate inference in order to be tractable. Instead of working in a combinatorial space, we use hinge-loss Markov random fields (HL-MRFs), an expressive class of graphical models with log-concave density functions over continuous variables, which can represent confidences in discrete predictions. This paper… CONTINUE READING

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