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A Ranking Theory for Uncertain Data with Constraints

A Ranking Theory for Uncertain Data with Constraints

作     者:Chonghai Wang is a graduate student in the Department of Computing Science University of Alberta Edmonton Li Yan Yuan is a professor in the Department of Computing Science University of Alberta Edmonton Jia-Huai You is a professor in the Department of Computing Science University of Alberta Edmonton 

会议名称:《2009 2nd IEEE International Conference on Computer Science and Information Technology(ICCSIT 2009)》

会议日期:2009年

学科分类:12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术(可授工学、理学学位)] 

关 键 词:top-k ranking uncertainty 

摘      要:We develop a theory of top-K ranking for objects whose values may be uncertain,incomplete,or difficult to be characterized quantitatively,but between which some constraints may be required to be *** present our ranking theory for discrete space,continuous space,and the general case with probability distributions and complex *** central question to be addressed is how to define the relative strengths of top-K object *** show that top-K ranking defined this way in continuous space is closely related to the analysis and computation of high dimensional polyhedra,and as a consequence,the methods for the latter can be applied to compute the support ratios of top-K object sequences so that the best can be chosen.

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