Сравнение на методи

	Полу-обучено трансферно обучение	Label Propagation
Област	Машинно обучение	Машинно обучение
Семейство	Machine learning	Machine learning
Година на възникване	2010s	2002
Създател	Pan, S. J. & Yang, Q. (formalized); wider community	Zhu, X. & Ghahramani, Z.
Тип	Hybrid learning paradigm	Graph-based semi-supervised classification
Основополагащ източник	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.	Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.
Други названия	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
Свързани	4	3
Резюме	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.