Spectral Regularization for Max-Margin Sequence Tagging
Abstract
We frame max-margin learning of latent variable structured prediction models as a convex optimization problem, making use of scoring functions computed by input-output observable operator models. This learning problem can be expressed as an optimization involving a low-rank Hankel matrix that represents the input-output operator model. The direct outcome of our work is a new spectral regularization method for max-margin structured prediction. Our experiments confirm that our proposed regularization framework leads to an effective way of controlling the capacity of structured prediction models.
Cite
Text
Quattoni et al. "Spectral Regularization for Max-Margin Sequence Tagging." International Conference on Machine Learning, 2014.Markdown
[Quattoni et al. "Spectral Regularization for Max-Margin Sequence Tagging." International Conference on Machine Learning, 2014.](https://mlanthology.org/icml/2014/quattoni2014icml-spectral/)BibTeX
@inproceedings{quattoni2014icml-spectral,
title = {{Spectral Regularization for Max-Margin Sequence Tagging}},
author = {Quattoni, Ariadna and Balle, Borja and Carreras, Xavier and Globerson, Amir},
booktitle = {International Conference on Machine Learning},
year = {2014},
pages = {1710-1718},
volume = {32},
url = {https://mlanthology.org/icml/2014/quattoni2014icml-spectral/}
}