Multi-Class Learning: From Theory to Algorithm
Abstract
In this paper, we study the generalization performance of multi-class classification and obtain a shaper data-dependent generalization error bound with fast convergence rate, substantially improving the state-of-art bounds in the existing data-dependent generalization analysis. The theoretical analysis motivates us to devise two effective multi-class kernel learning algorithms with statistical guarantees. Experimental results show that our proposed methods can significantly outperform the existing multi-class classification methods.
Cite
Text
Li et al. "Multi-Class Learning: From Theory to Algorithm." Neural Information Processing Systems, 2018.Markdown
[Li et al. "Multi-Class Learning: From Theory to Algorithm." Neural Information Processing Systems, 2018.](https://mlanthology.org/neurips/2018/li2018neurips-multiclass/)BibTeX
@inproceedings{li2018neurips-multiclass,
title = {{Multi-Class Learning: From Theory to Algorithm}},
author = {Li, Jian and Liu, Yong and Yin, Rong and Zhang, Hua and Ding, Lizhong and Wang, Weiping},
booktitle = {Neural Information Processing Systems},
year = {2018},
pages = {1586-1595},
url = {https://mlanthology.org/neurips/2018/li2018neurips-multiclass/}
}