Analysis of a Greedy Active Learning Strategy
Abstract
We abstract out the core search problem of active learning schemes, to better understand the extent to which adaptive labeling can improve sam- ple complexity. We give various upper and lower bounds on the number of labels which need to be queried, and we prove that a popular greedy active learning rule is approximately as good as any other strategy for minimizing this number of labels.
Cite
Text
Dasgupta. "Analysis of a Greedy Active Learning Strategy." Neural Information Processing Systems, 2004.Markdown
[Dasgupta. "Analysis of a Greedy Active Learning Strategy." Neural Information Processing Systems, 2004.](https://mlanthology.org/neurips/2004/dasgupta2004neurips-analysis/)BibTeX
@inproceedings{dasgupta2004neurips-analysis,
title = {{Analysis of a Greedy Active Learning Strategy}},
author = {Dasgupta, Sanjoy},
booktitle = {Neural Information Processing Systems},
year = {2004},
pages = {337-344},
url = {https://mlanthology.org/neurips/2004/dasgupta2004neurips-analysis/}
}