This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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532
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Type:
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Contributed
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Date/Time:
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Wednesday, August 4, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract - #308229 |
Title:
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Statistical Analysis of Bipartite and Multipartite Ranking by Convex Risk Minimization
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Author(s):
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Kazuki Uematsu*+ and Yoonkyung Lee
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Companies:
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The Ohio State University and The Ohio State University
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Address:
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, , ,
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Keywords:
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Bipartite Ranking ;
Consistency ;
Convex Risk Minimization ;
Multipartite Ranking ;
Ordinal Regression
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Abstract:
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There has been growing interest in the problem of ranking in machine learning and data mining for information retrieval and web search. As a special type of the problem, bipartite or multipartite ranking aims to learn a function which can rank instances using covariates in accordance with their ordered categorical responses such as movie ratings. We examine the theoretical relationship between a class of bipartite ranking loss criteria and the associated best ranking functions in the framework of convex risk minimization. The result illuminates the parallel between ranking and classification and suggests a notion of consistency. We further extend it to multipartite ranking by considering appropriate extensions of the ranking loss criteria based on AUC. The study provides a new perspective on ordinal regression and the proportional odds model for handling ordered categorical responses.
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