Uncertainty in Artificial Intelligence
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Batch-Mode Active Learning via Error Bound Minimization
Quanquan Gu, Tong Zhang, Jiawei Han
Active learning has been proven to be quite effec- tive in reducing the human labeling efforts by ac- tively selecting the most informative examples to label. In this paper, we present a batch-mode ac- tive learning method based on logistic regression. Our key motivation is an out-of-sample bound on the estimation error of class distribution in lo- gistic regression conditioned on any fixed train- ing sample. It is different from a typical PAC- style passive learning error bound, that relies on the i.i.d. assumption of example-label pairs. In addition, it does not contain the class labels of the training sample. Therefore, it can be imme- diately used to design an active learning algo- rithm by minimizing this bound iteratively. We also discuss the connections between the pro- posed method and some existing active learn- ing approaches. Experiments on benchmark UCI datasets and text datasets demonstrate that the proposed method outperforms the state-of-the-art active learning methods significantly.
Pages: 300-309
PS Link:
PDF Link: /papers/14/p300-gu.pdf
AUTHOR = "Quanquan Gu and Tong Zhang and Jiawei Han",
TITLE = "Batch-Mode Active Learning via Error Bound Minimization",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "300--309"

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