A kernel method for multi-labelled classification

Abstract

This article presents a Support Vector Machine (SVM) like learning system to handle multi-label problems. Such problems are usually decomposed 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 properties with SVMs. We tested it on a Yeast gene functional classification problem with positive results.

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Cite this paper

@inproceedings{Elisseeff2001AKM, title={A kernel method for multi-labelled classification}, author={Andr{\'e} Elisseeff and Jason Weston}, booktitle={NIPS}, year={2001} }