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    政大機構典藏 > 商學院 > 統計學系 > 期刊論文 >  Item 140.119/61613


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    题名: A Bayesian Approximation Method for Online Ranking
    作者: 翁久幸
    Weng,Ruby C.;Lin, Chih-Jen
    贡献者: 統計系
    关键词: Bayesian inference;rating system;Bradley-Terry model;Thurstone-Mosteller model;Plackett-Luce model
    日期: 2011.01
    上传时间: 2013-11-11 17:47:36 (UTC+8)
    摘要: This paper describes a Bayesian approximation method to obtain online ranking algorithms for games with multiple teams and multiple players. Recently for Internet games large online ranking systems are much needed. We consider game models in which a k-team game is treated as several two-team games. By approximating the expectation of teams` (or players`) performances, we derive simple analytic update rules. These update rules, without numerical integrations, are very easy to interpret and implement. Experiments on game data show that the accuracy of our approach is competitive with state of the art systems such as TrueSkill, but the running time as well as the code is much shorter.
    關聯: Journal of Machine Learning Research, 12 , 267-300
    数据类型: article
    显示于类别:[統計學系] 期刊論文

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