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CLC number: TP181

On-line Access: 2016-10-08

Received: 2016-03-10

Revision Accepted: 2016-09-14

Crosschecked: 2016-09-19

Cited: 1

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Citations:  Bibtex RefMan EndNote GB/T7714


Tao-cheng Hu


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Frontiers of Information Technology & Electronic Engineering  2016 Vol.17 No.10 P.973-981


Max-margin based Bayesian classifier

Author(s):  Tao-cheng Hu, Jin-hui Yu

Affiliation(s):  State Key Lab of CAD & CG, Zhejiang University, Hangzhou 310058, China

Corresponding email(s):   hutaocheng@gmail.com, jhyu@cad.zju.edu.cn

Key Words:  Multi-class learning, Max-margin learning, Online algorithm

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Tao-cheng Hu, Jin-hui Yu. Max-margin based Bayesian classifier[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(10): 973-981.

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There is a tradeoff between generalization capability and computational overhead in multi-class learning. We propose a generative probabilistic multi-class classifier, considering both the generalization capability and the learning/prediction rate. We show that the classifier has a max-margin property. Thus, prediction on future unseen data can nearly achieve the same performance as in the training stage. In addition, local variables are eliminated, which greatly simplifies the optimization problem. By convex and probabilistic analysis, an efficient online learning algorithm is developed. The algorithm aggregates rather than averages dualities, which is different from the classical situations. Empirical results indicate that our method has a good generalization capability and coverage rate.




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