Improving Classification with Pairwise Constraints: A Margin-Based Approach
Abstract
In this paper, we address the semi-supervised learning problem when there is a small amount of labeled data augmented with pairwise constraints indicating whether a pair of examples belongs to a same class or different classes. We introduce a discriminative learning approach that incorporates pairwise constraints into the conventional margin-based learning framework. We also present an efficient algorithm, PCSVM, to solve the pairwise constraint learning problem. Experiments with 15 data sets show that pairwise constraint information significantly increases the performance of classification.
Cite
Text
Nguyen and Caruana. "Improving Classification with Pairwise Constraints: A Margin-Based Approach." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2008. doi:10.1007/978-3-540-87481-2_8Markdown
[Nguyen and Caruana. "Improving Classification with Pairwise Constraints: A Margin-Based Approach." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2008.](https://mlanthology.org/ecmlpkdd/2008/nguyen2008ecmlpkdd-improving/) doi:10.1007/978-3-540-87481-2_8BibTeX
@inproceedings{nguyen2008ecmlpkdd-improving,
title = {{Improving Classification with Pairwise Constraints: A Margin-Based Approach}},
author = {Nguyen, Nam and Caruana, Rich},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2008},
pages = {113-124},
doi = {10.1007/978-3-540-87481-2_8},
url = {https://mlanthology.org/ecmlpkdd/2008/nguyen2008ecmlpkdd-improving/}
}