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. 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.
Cite
Text
Yu and Joachims. "Learning Structural SVMs with Latent Variables." International Conference on Machine Learning, 2009. doi:10.1145/1553374.1553523Markdown
[Yu and Joachims. "Learning Structural SVMs with Latent Variables." International Conference on Machine Learning, 2009.](https://mlanthology.org/icml/2009/yu2009icml-learning/) doi:10.1145/1553374.1553523BibTeX
@inproceedings{yu2009icml-learning,
title = {{Learning Structural SVMs with Latent Variables}},
author = {Yu, Chun-Nam John and Joachims, Thorsten},
booktitle = {International Conference on Machine Learning},
year = {2009},
pages = {1169-1176},
doi = {10.1145/1553374.1553523},
url = {https://mlanthology.org/icml/2009/yu2009icml-learning/}
}