NIPS Proceedingsβ

Multi-Class Learning: From Theory to Algorithm

Part of: Advances in Neural Information Processing Systems 31 (NIPS 2018)

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Conference Event Type: Poster


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.