Discriminative Experimental Design
Abstract
Since labeling data is often both laborious and costly, the labeled data available in many applications is rather limited. Active learning is a learning approach which actively selects unlabeled data points to label as a way to alleviate the labeled data deficiency problem. In this paper, we extend a previous active learning method called transductive experimental design (TED) by proposing a new unlabeled data selection criterion. Our method, called discriminative experimental design (DED), incorporates both margin-based discriminative information and data distribution information and hence it can be seen as a discriminative extension of TED. We report experiments conducted on some benchmark data sets to demonstrate the effectiveness of DED.
Cite
Text
Zhang and Yeung. "Discriminative Experimental Design." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2011. doi:10.1007/978-3-642-23808-6_38Markdown
[Zhang and Yeung. "Discriminative Experimental Design." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2011.](https://mlanthology.org/ecmlpkdd/2011/zhang2011ecmlpkdd-discriminative/) doi:10.1007/978-3-642-23808-6_38BibTeX
@inproceedings{zhang2011ecmlpkdd-discriminative,
title = {{Discriminative Experimental Design}},
author = {Zhang, Yu and Yeung, Dit-Yan},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2011},
pages = {585-596},
doi = {10.1007/978-3-642-23808-6_38},
url = {https://mlanthology.org/ecmlpkdd/2011/zhang2011ecmlpkdd-discriminative/}
}