Structured insight: an innovative disambiguation paradigm for semi-supervised partial label learning
Abstract Semi-Supervised Partial Label Learning (SPLL) aims to learn from both partial label data where each instance is associated with a candidate label set and unlabeled data. Most SPLL methods work by generating pseudo-candidate labels for unsupervised data. Since the pseudo candidate labels are...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-07-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44443-025-00119-x |
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