Many interesting multiclass problems can be cast in the general frame- work 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 Prefer- ence 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.

Learning Preferences for Multiclass Problems

AIOLLI, FABIO;SPERDUTI, ALESSANDRO
2005

Abstract

Many interesting multiclass problems can be cast in the general frame- work 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 Prefer- ence 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.
Advances in Neural Information Processing Systems 17
0262195348
9780262195348
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11577/1427698
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