Ensemble Methods for Structured Prediction
Abstract
We present a series of learning algorithms and theoretical guarantees for designing accurate ensembles of structured prediction tasks. This includes several randomized and deterministic algorithms devised by converting on-line learning algorithms to batch ones, and a boosting-style algorithm applicable in the context of structured prediction with a large number of labels. We give a detailed study of all these algorithms, including the description of new on-line-to-batch conversions and learning guarantees. We also report the results of extensive experiments with these algorithms in several structured prediction tasks.
Cite
Text
Cortes et al. "Ensemble Methods for Structured Prediction." International Conference on Machine Learning, 2014.Markdown
[Cortes et al. "Ensemble Methods for Structured Prediction." International Conference on Machine Learning, 2014.](https://mlanthology.org/icml/2014/cortes2014icml-ensemble/)BibTeX
@inproceedings{cortes2014icml-ensemble,
title = {{Ensemble Methods for Structured Prediction}},
author = {Cortes, Corinna and Kuznetsov, Vitaly and Mohri, Mehryar},
booktitle = {International Conference on Machine Learning},
year = {2014},
pages = {1134-1142},
volume = {32},
url = {https://mlanthology.org/icml/2014/cortes2014icml-ensemble/}
}