Classification by pairwise coupling
成果类型：
Article
署名作者：
Hastie, T; Tibshirani, R
署名单位：
Stanford University; University of Toronto
刊物名称：
ANNALS OF STATISTICS
ISSN/ISSBN：
0090-5364
发表日期：
1998
页码：
451-471
关键词：
摘要：
We discuss a strategy for polychotomous classification that involves estimating class probabilities for each pair of classes, and then coupling the estimates together. The coupling model is similar to the Bradley-Terry method for paired comparisons. We study the nature of the class probability estimates that arise, and examine the performance of the procedure in real and simulated data sets. Classifiers used include Linear discriminants, nearest neighbors, adaptive nonlinear methods and the support vector machine.