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	Weighted posterior probability output for support vector machines
	Zhang Xiang ; Xiao Xiaoling ; Xu Guangyou
	2010-05-06 ; 2010-05-06
关键词	Theoretical or Mathematical/ Bayes methods pattern classification statistical distributions support vector machines/ weighted posterior probability output support vector machines SVM Bayesian theory probability distribution voting method pairwise coupling method multiclass classifier/ C1250 Pattern recognition C1140Z Other topics in statistics C1230L Learning in AI
中文摘要	A weighted posterior probability method is presented to calculate the probability outputs of support vector machines (SVMs) for multi-class cases. The differences and weights for combination of the probability output among these two-class classifiers calculated from the posterior probability are given based on the Bayesian theory. Tests show that the weighted posterior probability method has less classification errors, better classification ability, and a better probability distribution of the posterior probability than the voting method or the Pairwise Coupling method. This method effectively provides probability outputs of SVMs in the multi-class case.
语种	中文 ; 中文
出版者	Tsinghua University Press ; China
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/10014]
专题	清华大学
推荐引用方式 GB/T 7714	Zhang Xiang,Xiao Xiaoling,Xu Guangyou. Weighted posterior probability output for support vector machines[J],2010, 2010.
APA	Zhang Xiang,Xiao Xiaoling,&Xu Guangyou.(2010).Weighted posterior probability output for support vector machines..
MLA	Zhang Xiang,et al."Weighted posterior probability output for support vector machines".(2010).

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