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| Max-margin based Bayesian classifier |
| Tao-cheng Hu, Jin-hui Yu |
| State Key Lab of CAD & CG, Zhejiang University, Hangzhou 310058, China |
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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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Received: 10 March 2016
Published: 08 October 2016
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基于最大间隔的贝叶斯分类器
\n 概要：多分类学习中经常需要考虑在泛化性能和计算开销间进行权衡。本文提出一个生成式概率多分类器，综合考虑了泛化性和学习/预测速率。我们首先证明了我们的分类器具有最大间隔性质，这意味着对于未来数据的预测精度几乎和训练阶段一样高。此外，我们消除了目标函数中的大量的局部变元，极大地简化了优化问题。通过凸分析和概率语义分析，我们设计了高效的在线算法，与经典情形的最大不同在于这个算法使用聚集而非平均化处理梯度。实验证明了我们的算法具有很好的泛化性能和收敛速度。
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关键词：
多类学习,
最大间隔学习,
在线算法
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