Learning Preferences for Multiclass Problems
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.
Cite
Text
Aiolli and Sperduti. "Learning Preferences for Multiclass Problems." Neural Information Processing Systems, 2004.Markdown
[Aiolli and Sperduti. "Learning Preferences for Multiclass Problems." Neural Information Processing Systems, 2004.](https://mlanthology.org/neurips/2004/aiolli2004neurips-learning/)BibTeX
@inproceedings{aiolli2004neurips-learning,
title = {{Learning Preferences for Multiclass Problems}},
author = {Aiolli, Fabio and Sperduti, Alessandro},
booktitle = {Neural Information Processing Systems},
year = {2004},
pages = {17-24},
url = {https://mlanthology.org/neurips/2004/aiolli2004neurips-learning/}
}