Toward Optimal Active Learning Through Sampling Estimation of Error Reduction
Abstract
This paper presents an active learning method that di-rectly optimizes expected future error. This is in con-trast to many other popular techniques that instead aim to reduce version space size. These other meth-ods are popular because for many learning models, closed form calculation of the expected future error is intractable. Our approach is made feasible by taking a sampling approach to estimating the expected reduc-tion in error due to the labeling of a query. In exper-imental results on two real-world data sets we reach high accuracy very quickly, sometimes with four times fewer labeled examples than competing methods. 1.
Cite
Text
Roy and McCallum. "Toward Optimal Active Learning Through Sampling Estimation of Error Reduction." International Conference on Machine Learning, 2001.Markdown
[Roy and McCallum. "Toward Optimal Active Learning Through Sampling Estimation of Error Reduction." International Conference on Machine Learning, 2001.](https://mlanthology.org/icml/2001/roy2001icml-optimal/)BibTeX
@inproceedings{roy2001icml-optimal,
title = {{Toward Optimal Active Learning Through Sampling Estimation of Error Reduction}},
author = {Roy, Nicholas and McCallum, Andrew},
booktitle = {International Conference on Machine Learning},
year = {2001},
pages = {441-448},
url = {https://mlanthology.org/icml/2001/roy2001icml-optimal/}
}