Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| Semi-supervised Transfer Learning× | Label Propagation× | |
|---|---|---|
| Fagfelt | Maskinlæring | Maskinlæring |
| Familie | Machine learning | Machine learning |
| Opprinnelsesår≠ | 2010s | 2002 |
| Opphavsperson≠ | Pan, S. J. & Yang, Q. (formalized); wider community | Zhu, X. & Ghahramani, Z. |
| Type≠ | Hybrid learning paradigm | Graph-based semi-supervised classification |
| Opprinnelig kilde≠ | Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., & He, Q. (2021). A comprehensive survey on transfer learning. Proceedings of the IEEE, 109(1), 43–76. DOI ↗ | Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University. link ↗ |
| Alias | SSTL, semi-supervised domain adaptation, transfer learning with unlabeled data, few-label transfer learning | LP, label spreading, graph-based semi-supervised learning, harmonic label propagation |
| Relaterte≠ | 4 | 3 |
| Sammendrag≠ | Semi-supervised Transfer Learning combines knowledge transferred from a richly labeled source domain with the structure of abundant unlabeled target-domain data, using only a small set of labeled target examples to achieve strong generalization where full annotation is scarce or expensive. | Label Propagation is a graph-based semi-supervised learning algorithm introduced by Zhu and Ghahramani in 2002 that spreads class labels from a small set of labeled nodes to a large set of unlabeled nodes by iteratively diffusing label information along the edges of a similarity graph, exploiting the manifold structure of the data. |
| ScholarGateDatasett ↗ |
|
|