A Continuation Method for Semi-Supervised SVMs
Abstract
Semi-Supervised Support Vector Machines (S3VMs) are an appealing method for using unlabeled data in classification: their objective function favors decision boundaries which do not cut clusters. However their main problem is that the optimization problem is non-convex and has many local minima, which often results in suboptimal performances. In this paper we propose to use a global optimization technique known as continuation to alleviate this problem. Compared to other algorithms minimizing the same objective function, our continuation method often leads to lower test errors.
Cite
Text
Chapelle et al. "A Continuation Method for Semi-Supervised SVMs." International Conference on Machine Learning, 2006. doi:10.1145/1143844.1143868Markdown
[Chapelle et al. "A Continuation Method for Semi-Supervised SVMs." International Conference on Machine Learning, 2006.](https://mlanthology.org/icml/2006/chapelle2006icml-continuation/) doi:10.1145/1143844.1143868BibTeX
@inproceedings{chapelle2006icml-continuation,
title = {{A Continuation Method for Semi-Supervised SVMs}},
author = {Chapelle, Olivier and Chi, Mingmin and Zien, Alexander},
booktitle = {International Conference on Machine Learning},
year = {2006},
pages = {185-192},
doi = {10.1145/1143844.1143868},
url = {https://mlanthology.org/icml/2006/chapelle2006icml-continuation/}
}