Groupwise Ranking Loss for Multi-Label Learning
This work studies multi-label learning (MLL), where each instance is associated with a subset of positive labels. For each instance, a good multi-label predictor should encourage the predicted positive labels to be close to its ground-truth positive ones. In this work, we propose a new loss, named G...
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| Main Authors: | , , , , , |
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| Format: | Artigo |
| Language: | Inglês |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8970478/ |
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