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Online choice of active learning algorithms (2003)
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| Venue: | Journal of Machine Learning Research |
| Citations: | 118 - 2 self |
BibTeX
@ARTICLE{Baram03onlinechoice,
author = {Yoram Baram and Ran El-yaniv and Kobi Luz},
title = {Online choice of active learning algorithms},
journal = {Journal of Machine Learning Research},
year = {2003},
volume = {5},
pages = {2004}
}
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Abstract
This paper is concerned with the question of how to online combine an ensemble of active learners so as to expedite the learning progress during a pool-based active learning session. We develop a powerful active learning master algorithm, based a known competitive algorithm for the multi-armed bandit problem and a novel semi-supervised performance evaluation statistic. Taking an ensemble containing two of the best known active learning algorithms and a new algorithm, the resulting new active learning master algorithm is empirically shown to consistently perform almost as well as and sometimes outperform the best algorithm in the ensemble on a range of classification problems. 1.
Keyphrases
active learning algorithm online choice powerful active learning master algorithm known competitive algorithm active learner learning progress new active learning master algorithm multi-armed bandit problem new algorithm classification problem novel semi-supervised performance evaluation statistic pool-based active learning session
