CLC number: TP181
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2016-09-19
Cited: 1
Clicked: 6368
Tao-cheng Hu, Jin-hui Yu. Max-margin based Bayesian classifier[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(10): 973-981.
@article{title="Max-margin based Bayesian classifier",
author="Tao-cheng Hu, Jin-hui Yu",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="17",
number="10",
pages="973-981",
year="2016",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1601078"
}
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T1 - Max-margin based Bayesian classifier
A1 - Tao-cheng Hu
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Y1 - 2016
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1601078
Abstract: 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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