Ensemble Methods for Structured Prediction

Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri
Proceedings of the 31st International Conference on Machine Learning, PMLR 32(2):1134-1142, 2014.

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 this Paper

BibTeX
@InProceedings{pmlr-v32-cortesa14, title = {Ensemble Methods for Structured Prediction}, author = {Cortes, Corinna and Kuznetsov, Vitaly and Mohri, Mehryar}, booktitle = {Proceedings of the 31st International Conference on Machine Learning}, pages = {1134--1142}, year = {2014}, editor = {Xing, Eric P. and Jebara, Tony}, volume = {32}, number = {2}, series = {Proceedings of Machine Learning Research}, address = {Bejing, China}, month = {22--24 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v32/cortesa14.pdf}, url = {https://proceedings.mlr.press/v32/cortesa14.html}, 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.} }
Endnote
%0 Conference Paper %T Ensemble Methods for Structured Prediction %A Corinna Cortes %A Vitaly Kuznetsov %A Mehryar Mohri %B Proceedings of the 31st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2014 %E Eric P. Xing %E Tony Jebara %F pmlr-v32-cortesa14 %I PMLR %P 1134--1142 %U https://proceedings.mlr.press/v32/cortesa14.html %V 32 %N 2 %X 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.
RIS
TY - CPAPER TI - Ensemble Methods for Structured Prediction AU - Corinna Cortes AU - Vitaly Kuznetsov AU - Mehryar Mohri BT - Proceedings of the 31st International Conference on Machine Learning DA - 2014/06/18 ED - Eric P. Xing ED - Tony Jebara ID - pmlr-v32-cortesa14 PB - PMLR DP - Proceedings of Machine Learning Research VL - 32 IS - 2 SP - 1134 EP - 1142 L1 - http://proceedings.mlr.press/v32/cortesa14.pdf UR - https://proceedings.mlr.press/v32/cortesa14.html AB - 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. ER -
APA
Cortes, C., Kuznetsov, V. & Mohri, M.. (2014). Ensemble Methods for Structured Prediction. Proceedings of the 31st International Conference on Machine Learning, in Proceedings of Machine Learning Research 32(2):1134-1142 Available from https://proceedings.mlr.press/v32/cortesa14.html.

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