Information, Prediction, and Query by Committee
Abstract
We analyze the "query by committee" algorithm, a method for fil(cid:173) tering informative queries from a random stream of inputs. We show that if the two-member committee algorithm achieves infor(cid:173) mation gain with positive lower bound, then the prediction error decreases exponentially with the number of queries. We show that, in particular, this exponential decrease holds for query learning of thresholded smooth functions.
Cite
Text
Freund et al. "Information, Prediction, and Query by Committee." Neural Information Processing Systems, 1992.Markdown
[Freund et al. "Information, Prediction, and Query by Committee." Neural Information Processing Systems, 1992.](https://mlanthology.org/neurips/1992/freund1992neurips-information/)BibTeX
@inproceedings{freund1992neurips-information,
title = {{Information, Prediction, and Query by Committee}},
author = {Freund, Yoav and Seung, H. Sebastian and Shamir, Eli and Tishby, Naftali},
booktitle = {Neural Information Processing Systems},
year = {1992},
pages = {483-490},
url = {https://mlanthology.org/neurips/1992/freund1992neurips-information/}
}