A Kernel Method for Multi-Labelled Classification
Abstract
This article presents a Support Vector Machine (SVM) like learning sys- tem to handle multi-label problems. Such problems are usually decom- posed into many two-class problems but the expressive power of such a system can be weak [5, 7]. We explore a new direct approach. It is based on a large margin ranking system that shares a lot of common proper- ties with SVMs. We tested it on a Yeast gene functional classification problem with positive results.
Cite
Text
Elisseeff and Weston. "A Kernel Method for Multi-Labelled Classification." Neural Information Processing Systems, 2001.Markdown
[Elisseeff and Weston. "A Kernel Method for Multi-Labelled Classification." Neural Information Processing Systems, 2001.](https://mlanthology.org/neurips/2001/elisseeff2001neurips-kernel/)BibTeX
@inproceedings{elisseeff2001neurips-kernel,
title = {{A Kernel Method for Multi-Labelled Classification}},
author = {Elisseeff, André and Weston, Jason},
booktitle = {Neural Information Processing Systems},
year = {2001},
pages = {681-687},
url = {https://mlanthology.org/neurips/2001/elisseeff2001neurips-kernel/}
}