Learning structural SVMs with latent variables

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.

DOI: 10.1145/1553374.1553523

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Large margin rank boundaries for ordinal regression

  • R Herbrich, T Graepel, K Obermayer
  • 2000
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