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2004 JMLR JMLR 2004

Probability Estimates for Multi-class Classification by Pairwise Coupling

Abstract

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). [abs] [ pdf ][ ps.gz ][ ps ]

🧭 Keyword Pioneer — linear system
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio
📈 Trend Setter — Support Vector Machine
🐣 Hot Topic Early Bird — multi-class classification

Authors

Ting-Fan Wu , Chih-Jen Lin , Ruby C. Weng

Topics

Machine Learning > Core Methods > Classification Machine Learning > Core Methods > Support Vector Machine

Keywords

multi-class classification support vector machine linear system pairwise coupling class probability discriminant classification
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