Learning Preferences for Multiclass Problems

Many interesting multiclass problems can be cast in the general framework of label ranking defined on a given set of classes. The evaluation for such a ranking is generally given in terms of the number of violated order constraints between classes. In this paper, we propose the Preference Learning Model as a unifying framework to model and solve a large class of multiclass problems in a large margin perspective. In addition, an original kernel-based method is proposed and evaluated on a ranking dataset with state-of-the-art results.

• Type: Conference
• Year: 2004
• Where: NIPS
• Authors: Fabio Aiolli, Alessandro Sperduti
• Keywords: Many Interesting Multiclass, Multiclass Problems, NIPS 2004, NIPS 2007, Ranking