Machine learningMachine learning
贝叶斯迁移学习
贝叶斯迁移学习是一种概率框架，它利用来自数据丰富的源域的知识，为在数据稀缺的目标域上训练的模型构建信息先验。通过将源域知识编码为参数的先验分布，该框架使模型即使在标记样本非常有限的情况下，也能在目标任务上很好地泛化。
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来源
- 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. link ↗
- Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI: 10.1109/TKDE.2009.191 ↗
如何引用本页
ScholarGate. (2026, June 3). Bayesian Transfer Learning (Probabilistic Domain Adaptation). ScholarGate. https://scholargate.app/zh/machine-learning/bayesian-transfer-learning
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