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Learning Structural SVMs with Latent Variables
This video was recorded at 26th International Conference on Machine Learning (ICML), Montreal 2009. 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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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
