Using Unlabeled Data for Supervised Learning
Abstract
Many classification problems have the property that the only costly part of obtaining examples is the class label. This paper suggests a simple method for using distribution information contained in unlabeled examples to augment labeled examples in a supervised training framework. Empirical tests show that the technique de(cid:173) scribed in this paper can significantly improve the accuracy of a supervised learner when the learner is well below its asymptotic accuracy level.
Cite
Text
Towell. "Using Unlabeled Data for Supervised Learning." Neural Information Processing Systems, 1995.Markdown
[Towell. "Using Unlabeled Data for Supervised Learning." Neural Information Processing Systems, 1995.](https://mlanthology.org/neurips/1995/towell1995neurips-using/)BibTeX
@inproceedings{towell1995neurips-using,
title = {{Using Unlabeled Data for Supervised Learning}},
author = {Towell, Geoffrey G.},
booktitle = {Neural Information Processing Systems},
year = {1995},
pages = {647-653},
url = {https://mlanthology.org/neurips/1995/towell1995neurips-using/}
}