Active Learning for Identifying Function Threshold Boundaries
Abstract
We present an efficient algorithm to actively select queries for learning the boundaries separating a function domain into regions where the func- tion is above and below a given threshold. We develop experiment selec- tion methods based on entropy, misclassification rate, variance, and their combinations, and show how they perform on a number of data sets. We then show how these algorithms are used to determine simultaneously valid 1 − α confidence intervals for seven cosmological parameters. Ex- perimentation shows that the algorithm reduces the computation neces- sary for the parameter estimation problem by an order of magnitude.
Cite
Text
Bryan et al. "Active Learning for Identifying Function Threshold Boundaries." Neural Information Processing Systems, 2005.Markdown
[Bryan et al. "Active Learning for Identifying Function Threshold Boundaries." Neural Information Processing Systems, 2005.](https://mlanthology.org/neurips/2005/bryan2005neurips-active/)BibTeX
@inproceedings{bryan2005neurips-active,
title = {{Active Learning for Identifying Function Threshold Boundaries}},
author = {Bryan, Brent and Nichol, Robert C. and Genovese, Christopher R and Schneider, Jeff and Miller, Christopher J. and Wasserman, Larry},
booktitle = {Neural Information Processing Systems},
year = {2005},
pages = {163-170},
url = {https://mlanthology.org/neurips/2005/bryan2005neurips-active/}
}