Learning structural SVMs with latent variables

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 ConcaveConvex 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.

• Type: Conference
• Year: 2009
• Where: ICML
• Authors: Chun-Nam John Yu, Thorsten Joachims
• Keywords: ICML 2009, Latent Variables, Machine Learning, Noun-phrase Coreference Resolution, Structured Output Prediction