Presentation | 2012-11-08 Efficient AUC Maximization by Approximate Reduction of Ranking SVMs Daiki SUEHIRO, Kohei HATANO, Eiji TAKIMOTO, |
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Abstract(in English) | The formulation of Ranking SVMs is popular for maximizing AUC scores. More precisely, the formulation is given as a hard/soft margin optimization over pn pairs of p positive and n negative instances. Directly solving the problem is impractical since we have to deal with a sample of size pn, which is quadratically larger than the original sample size p+n. In this paper, we propose (approximate) reduction methods from the hard/soft margin optimization over pn pairs to variants of hard/soft margin optimization over p+n instances. The resulting classifiers of our methods are guaranteed to have a certain amount of margin over pn pairs. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | bipartite ranking / AUC / reduction / Ranking SVMs |
Paper # | IBISML2012-68 |
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Committee | IBISML |
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Conference Date | 2012/10/31(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Information-Based Induction Sciences and Machine Learning (IBISML) |
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Language | ENG |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Efficient AUC Maximization by Approximate Reduction of Ranking SVMs |
Sub Title (in English) | |
Keyword(1) | bipartite ranking |
Keyword(2) | AUC |
Keyword(3) | reduction |
Keyword(4) | Ranking SVMs |
1st Author's Name | Daiki SUEHIRO |
1st Author's Affiliation | Department of Informatics, Kyushu University() |
2nd Author's Name | Kohei HATANO |
2nd Author's Affiliation | Department of Informatics, Kyushu University |
3rd Author's Name | Eiji TAKIMOTO |
3rd Author's Affiliation | Department of Informatics, Kyushu University |
Date | 2012-11-08 |
Paper # | IBISML2012-68 |
Volume (vol) | vol.112 |
Number (no) | 279 |
Page | pp.pp.- |
#Pages | 7 |
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