A kernel method for multi-labelled classification

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.

• Type: Conference
• Year: 2001
• Where: NIPS
• Authors: André Elisseeff, Jason Weston
• Keywords: Gene Functional Classification, NIPS 2001, NIPS 2007, Such Problems, Support Vector Machine