方法对比

	贝叶斯迁移学习	半监督迁移学习
领域	机器学习	机器学习
方法族	Machine learning	Machine learning
起源年份	2006–2010	2010s
提出者	Raina, R.; Ng, A. Y.; Koller, D. (and subsequent community)	Pan, S. J. & Yang, Q. (formalized); wider community
类型	Probabilistic transfer / domain adaptation framework	Hybrid learning paradigm
开创性文献	Raina, R., Ng, A. Y., & Koller, D. (2006). Constructing informative priors using transfer learning. In Proceedings of the 23rd International Conference on Machine Learning (ICML), pp. 713–720. ACM.	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.
别名	BTL, Bayesian domain adaptation, probabilistic transfer learning, Bayesian knowledge transfer	SSTL, semi-supervised domain adaptation, transfer learning with unlabeled data, few-label transfer learning
相关	4	4
摘要	Bayesian Transfer Learning is a probabilistic framework that uses knowledge from a data-rich source domain to construct informative priors for a model trained on a data-scarce target domain. By encoding source-domain knowledge as prior distributions over parameters, the framework lets the model generalize well on the target task even with very limited labeled examples.	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.