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240125s2024 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2024.3357814
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|a eng
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1 |
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|a Xie, Zheng
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Weakly Supervised AUC Optimization
|b A Unified Partial AUC Approach
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|c 2024
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|a Text
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|a ƒaComputermedien
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|a Date Revised 06.06.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Since acquiring perfect supervision is usually difficult, real-world machine learning tasks often confront inaccurate, incomplete, or inexact supervision, collectively referred to as weak supervision. In this work, we present WSAUC, a unified framework for weakly supervised AUC optimization problems, which covers noisy label learning, positive-unlabeled learning, multi-instance learning, and semi-supervised learning scenarios. Within the WSAUC framework, we first frame the AUC optimization problems in various weakly supervised scenarios as a common formulation of minimizing the AUC risk on contaminated sets, and demonstrate that the empirical risk minimization problems are consistent with the true AUC. Then, we introduce a new type of partial AUC, specifically, the reversed partial AUC (rpAUC), which serves as a robust training objective for AUC maximization in the presence of contaminated labels. WSAUC offers a universal solution for AUC optimization in various weakly supervised scenarios by maximizing the empirical rpAUC. Theoretical and experimental results under multiple settings support the effectiveness of WSAUC on a range of weakly supervised AUC optimization tasks
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|a Journal Article
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| 700 |
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|a Liu, Yu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a He, Hao-Yuan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Ming
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhou, Zhi-Hua
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 46(2024), 7 vom: 24. Juli, Seite 4780-4795
|w (DE-627)NLM098212257
|x 1939-3539
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|g year:2024
|g number:7
|g day:24
|g month:07
|g pages:4780-4795
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|u http://dx.doi.org/10.1109/TPAMI.2024.3357814
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