Poster
Multi-Class Learning: From Theory to Algorithm
Jian Li · Yong Liu · Rong Yin · Hua Zhang · Lizhong Ding · Weiping Wang
Room 210 #100
Keywords: [ Large Margin Methods ] [ Classification ] [ Learning Theory ]
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.
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