Machine learningMachine learning
半监督迁移学习
半监督迁移学习将来自丰富标注源域的知识与海量无标注目标域数据的结构相结合，仅使用少量标注的目标样本即可在标注稀缺或昂贵的场景下实现强大的泛化能力。
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来源
- 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: 10.1109/JPROC.2020.3004555 ↗
- Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
如何引用本页
ScholarGate. (2026, June 3). Semi-supervised Transfer Learning. ScholarGate. https://scholargate.app/zh/machine-learning/semi-supervised-transfer-learning
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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