Machine learningMachine learning
稳健自举聚合
Robust Bagging 扩展了经典的 Bootstrap Aggregating (Bagging) 框架，通过替换或增强标准的基学习器（base learners）为鲁棒估计器（robust estimators），或使用鲁棒的聚合规则（robust aggregation rules），使得即使在训练数据包含异常值、错误标记的实例或重尾噪声分布时，集成模型（ensemble）仍能保持准确性。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140. DOI: 10.1007/BF00058655 ↗
- Chen, C., Liaw, A., & Breiman, L. (2004). Using Random Forest to Learn Imbalanced Data. University of California, Berkeley, Technical Report 666. link ↗
如何引用本页
ScholarGate. (2026, June 3). Robust Bagging (Bootstrap Aggregating with Robust Base Learners). ScholarGate. https://scholargate.app/zh/machine-learning/robust-bagging
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.
- Bagging（Bootstrap Aggregating）机器学习↔ compare
- Boosting机器学习↔ compare
- 随机森林机器学习↔ compare
- 鲁棒提升机器学习↔ compare
- 鲁棒随机森林机器学习↔ compare
- 投票集成 (Voting Ensemble)机器学习↔ compare