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    题名: Generalized Bradley-Terry Models and Multi-Class Probability Estimates
    作者: Weng, Ruby C.;Huang, Tzu-kuo;Lin, Chih-jen
    翁久幸
    贡献者: 統計系
    关键词: Bradley-Terry model;probability estimates;error correcting output codes;support vector machines
    日期: 2006
    上传时间: 2015-04-07 17:02:11 (UTC+8)
    摘要: The Bradley-Terry model for obtaining individual skill from paired comparisons has been popular in many areas. In machine learning, this model is related to multi-class probability estimates by coupling all pairwise classification results. Error correcting output codes (ECOC) are a general framework to decompose a multi-class problem to several binary problems. To obtain probability estimates under this framework, this paper introduces a generalized Bradley-Terry model in which paired individual comparisons are extended to paired team comparisons. We propose a simple algorithm with convergence proofs to solve the model and obtain individual skill. Experiments on synthetic and real data demonstrate that the algorithm is useful for obtaining multi-class probability estimates. Moreover, we discuss four extensions of the proposed model: 1) weighted individual skill, 2) home-field advantage, 3) ties, and 4) comparisons with more than two teams.
    關聯: Journal of Machine Learning Research - JMLR , vol. 7, pp. 85-115
    数据类型: article
    显示于类别:[統計學系] 期刊論文

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