Learning structural SVMs with latent variables

Authors:
Chun-Nam John Yu;Thorsten Joachims
Affiliations:
Cornell University, Ithaca, NY;Cornell University, Ithaca, NY
Venue:
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Year:
2009

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Abstract

We present a large-margin formulation and algorithm for structured output prediction that allows the use of latent variables. Our proposal covers a large range of application problems, with an optimization problem that can be solved efficiently using Concave-Convex Programming. The generality and performance of the approach is demonstrated through three applications including motiffinding, noun-phrase coreference resolution, and optimizing precision at k in information retrieval.