Loading...
Please wait, while we are loading the content...
Similar Documents
Learning Structural SVMs with Latent Variables
| Content Provider | CiteSeerX |
|---|---|
| Author | Joachims, Thorsten Yu, Chun-Nam John |
| 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. 1. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Large Range Concave-convex Programming Optimization Problem Structured Output Prediction Large-margin Formulation Noun-phrase Coreference Resolution Latent Variable Structural Svms Application Problem |
| Content Type | Text |