Semi-Supervised Regression via Hierarchical Confidence Screening and Neighbor-Guided Pseudo-Label Calibration
Semi-supervised regression enhances model performance by effectively utilizing unlabeled data during training. Most existing methods select only a small subset of high-confidence unlabeled samples based on single confidence metrics to participate in model training. However, these approaches suffer f...
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| Autori principali: | , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
IEEE
2026-01-01
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| Serie: | IEEE Access |
| Soggetti: | |
| Accesso online: | https://ieeexplore.ieee.org/document/11474475/ |
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