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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Hlavní autoři:	Xin Niu, Jing Chai, Musaed Alhussein, Khursheed Aurangzeb
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	Springer 2025-07-01
Edice:	Journal of King Saud University: Computer and Information Sciences
Témata:	Partial label learning Semi-supervised learning Pseudo candidate labels Confidence matrix
On-line přístup:	https://doi.org/10.1007/s44443-025-00119-x
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