Optimization Techniques for Semi-Supervised Support Vector Machines
Abstract
Due to its wide applicability, the problem of semi-supervised classification is attracting increasing attention in machine learning. Semi-Supervised Support Vector Machines (S3VMs) are based on applying the margin maximization principle to both labeled and unlabeled examples. Unlike SVMs, their formulation leads to a non-convex optimization problem. A suite of algorithms have recently been proposed for solving S3VMs. This paper reviews key ideas in this literature. The performance and behavior of various S3VMs algorithms is studied together, under a common experimental setting.
Cite
Text
Chapelle et al. "Optimization Techniques for Semi-Supervised Support Vector Machines." Journal of Machine Learning Research, 2008.Markdown
[Chapelle et al. "Optimization Techniques for Semi-Supervised Support Vector Machines." Journal of Machine Learning Research, 2008.](https://mlanthology.org/jmlr/2008/chapelle2008jmlr-optimization/)BibTeX
@article{chapelle2008jmlr-optimization,
title = {{Optimization Techniques for Semi-Supervised Support Vector Machines}},
author = {Chapelle, Olivier and Sindhwani, Vikas and Keerthi, Sathiya S.},
journal = {Journal of Machine Learning Research},
year = {2008},
pages = {203-233},
volume = {9},
url = {https://mlanthology.org/jmlr/2008/chapelle2008jmlr-optimization/}
}