Efficient Co-Training of Linear Separators Under Weak Dependence
Abstract
We develop the first polynomial-time algorithm for co-training of homogeneous linear separators under \em weak dependence, a relaxation of the condition of independence given the label. Our algorithm learns from purely unlabeled data, except for a single labeled example to break symmetry of the two classes, and works for any data distribution having an inverse-polynomial margin and with center of mass at the origin.
Cite
Text
Blum and Mansour. "Efficient Co-Training of Linear Separators Under Weak Dependence." Proceedings of the 2017 Conference on Learning Theory, 2017.Markdown
[Blum and Mansour. "Efficient Co-Training of Linear Separators Under Weak Dependence." Proceedings of the 2017 Conference on Learning Theory, 2017.](https://mlanthology.org/colt/2017/blum2017colt-efficient/)BibTeX
@inproceedings{blum2017colt-efficient,
title = {{Efficient Co-Training of Linear Separators Under Weak Dependence}},
author = {Blum, Avrim and Mansour, Yishay},
booktitle = {Proceedings of the 2017 Conference on Learning Theory},
year = {2017},
pages = {302-318},
volume = {65},
url = {https://mlanthology.org/colt/2017/blum2017colt-efficient/}
}