Multi-Class Learning: From Theory to Algorithm
Published in Advances in Neural Information Processing Systems 31 (NIPS 2018), 2018
Jian Li, Yong Liu, Rong Yin, Hua Zhang, Lizhong Ding, Weiping Wang. Advances in Neural Information Processing Systems 31 (NIPS 2018)..
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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.