Efficient Semi-Supervised and Active Learning of Disjunctions
Abstract
We provide efficient algorithms for learning disjunctions in the semi-supervised setting under a natural regularity assumption introduced by (Balcan & Blum, 2005). We prove bounds on the sample complexity of our algorithms under a mild restriction on the data distribution. We also give an active learning algorithm with improved sample complexity and extend all our algorithms to the random classification noise setting.
Cite
Text
Balcan et al. "Efficient Semi-Supervised and Active Learning of Disjunctions." International Conference on Machine Learning, 2013.Markdown
[Balcan et al. "Efficient Semi-Supervised and Active Learning of Disjunctions." International Conference on Machine Learning, 2013.](https://mlanthology.org/icml/2013/balcan2013icml-efficient/)BibTeX
@inproceedings{balcan2013icml-efficient,
title = {{Efficient Semi-Supervised and Active Learning of Disjunctions}},
author = {Balcan, Nina and Berlind, Christopher and Ehrlich, Steven and Liang, Yingyu},
booktitle = {International Conference on Machine Learning},
year = {2013},
pages = {633-641},
volume = {28},
url = {https://mlanthology.org/icml/2013/balcan2013icml-efficient/}
}