Probability estimates for multi-class classification by pairwise coupling

被引：0

作者：

Wu, Ting-Fan ^{[1
]}

Lin, Chih-Jen ^{[1
]}

Weng, Ruby C. ^{[2
]}

机构：

[1] Department of Computer Science, National Taiwan University, Taipei,106, Taiwan

[2] Department of Statistics, National Chengchi University, Taipei,116, Taiwan

来源：

Journal of Machine Learning Research | 2004年 / 5卷

关键词：

Classification (of information) - Probability - Learning systems - Classifiers - Support vector machines - Decision trees;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

Pairwise coupling is a popular multi-class classification method that combines all comparisons for each pair of classes. This paper presents two approaches for obtaining class probabilities. Both methods can be reduced to linear systems and are easy to implement. We show conceptually and experimentally that the proposed approaches are more stable than the two existing popular methods: voting and the method by Hastie and Tibshirani (1998). © 2004 Ting-Fan Wu, Chih-Jen Lin, and Ruby C. Weng.

引用

页码：975 / 1005

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