A Simple Probabilistic Approach to Learning from Positive and Unlabeled Examples (2005)
by
Dell Zhang
| Venue: | In Proc. of the 5th Annual UK Workshop on Computational Intelligence |
| Citations: | 5 - 2 self |
BibTeX
@INPROCEEDINGS{Zhang05asimple,
author = {Dell Zhang},
title = {A Simple Probabilistic Approach to Learning from Positive and Unlabeled Examples},
booktitle = {In Proc. of the 5th Annual UK Workshop on Computational Intelligence},
year = {2005}
}
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Abstract
We propose a simple probabilistic approach to learning from positive and unlabeled examples, and show experimentally that it can approximate or outperform other state-ofthe-art approaches to this problem in spite of its simplicity. By employing a linear-time learning algorithm such as PrTFIDF, our approach can be highly efficient and scalable. 1
