Learning Structural SVMs with Latent Variables

author: Chun-Nam Yu, Department of Computer Science, Cornell University
published: Sept. 17, 2009,   recorded: June 2009,   views: 10134


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We present a large-margin formulation and algorithm for structured output prediction that allows the use of latent variables. The paper identifies a particular formulation that covers a large range of application problems, while showing that the resulting optimization problem can generally be addressed using Concave-Convex Programming. The generality and performance of the approach is demonstrated on a motif-finding application, noun-phrase coreference resolution, and optimizing precision at k in information retrieval.

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