Machine learningMachine learning
半监督提升
半监督提升是一种集成学习范式，它扩展了经典的提升算法（如AdaBoost），以同时利用标记和未标记数据。通过在未标记实例的相似性结构上传播标签信息，它可以在标记数据稀缺时训练出比单独的监督提升更强的分类器。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Mallapragada, P. K., Jin, R., Jain, A. K., & Liu, Y. (2009). SemiBoost: Boosting for Semi-supervised Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(11), 2000–2014. DOI: 10.1109/TPAMI.2008.235 ↗
- Bennett, K. P., & Demiriz, A. (1999). Semi-supervised Support Vector Machines. Advances in Neural Information Processing Systems (NIPS), 11, 368–374. link ↗
如何引用本页
ScholarGate. (2026, June 3). Semi-supervised Boosting (Boosting with Unlabeled Data). ScholarGate. https://scholargate.app/zh/machine-learning/semi-supervised-boosting
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.
- AdaBoost机器学习↔ compare
- 梯度提升（Gradient Boosting）机器学习↔ compare
- 标签传播机器学习↔ compare
- 半监督学习机器学习↔ compare
- XGBoost机器学习↔ compare