A Unified View on Multi-Class Support Vector Classification
Abstract
A unified view on multi-class support vector machines (SVMs) is presented, covering most prominent variants including the one- vs-all approach and the algorithms proposed by Weston & Watkins, Crammer & Singer, Lee, Lin, & Wahba, and Liu & Yuan. The unification leads to a template for the quadratic training problems and new multi-class SVM formulations. Within our framework, we provide a comparative analysis of the various notions of multi-class margin and margin-based loss. In particular, we demonstrate limitations of the loss function considered, for instance, in the Crammer & Singer machine.
Cite
Text
Doğan et al. "A Unified View on Multi-Class Support Vector Classification." Journal of Machine Learning Research, 2016.Markdown
[Doğan et al. "A Unified View on Multi-Class Support Vector Classification." Journal of Machine Learning Research, 2016.](https://mlanthology.org/jmlr/2016/dogan2016jmlr-unified/)BibTeX
@article{dogan2016jmlr-unified,
title = {{A Unified View on Multi-Class Support Vector Classification}},
author = {Doğan, Ürün and Glasmachers, Tobias and Igel, Christian},
journal = {Journal of Machine Learning Research},
year = {2016},
pages = {1-32},
volume = {17},
url = {https://mlanthology.org/jmlr/2016/dogan2016jmlr-unified/}
}