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.

Extracted Key Phrases

Showing 1-10 of 345 extracted citations
Citations per Year

817 Citations

Semantic Scholar estimates that this publication has received between 659 and 1,008 citations based on the available data.

See our FAQ for additional information.