Machine learningMachine learning
正则化迁移学习
正则化迁移学习在迁移学习流程中应用显式的惩罚项，以控制模型在适应新目标域时偏离源域知识的程度。正则化器可以抑制负迁移——即不相关源域模式的有害遗留——同时保留有益的共享表示，并在目标域标签稀缺时防止过拟合。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- 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 ↗
- Li, Z., Nie, F., Chang, X., & Yang, Y. (2014). Beyond trace norm: Robust matrix recovery via bi-sparsity pursuit. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 1736–1742. link ↗
如何引用本页
ScholarGate. (2026, June 3). Regularized Transfer Learning (Regularization-Constrained Domain Adaptation). ScholarGate. https://scholargate.app/zh/machine-learning/regularized-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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- 半监督迁移学习机器学习↔ compare
- 迁移学习机器学习↔ compare