Machine learningMachine learning
在线Bagging
在线Bagging是由Oza和Russell在2001年提出的一种流式集成方法，它将经典的bootstrap聚合（Bagging）框架适应于在线学习场景。每个传入的实例不是通过重采样固定数据集获得，而是以服从泊松分布（泊松(1)）的次数被馈送到每个基学习器中，从而在流演进过程中忠实地近似bootstrap采样。其结果是一个鲁棒的、增量更新的集成模型，能够处理概念漂移和连续数据到达，而无需存储整个数据集。
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Method map
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来源
- Oza, N. C., & Russell, S. (2001). Online bagging and boosting. In Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics (AISTATS 2001), pp. 105–112. link ↗
- Bifet, A., Holmes, G., Kirkby, R., & Pfahringer, B. (2010). MOA: Massive Online Analysis. Journal of Machine Learning Research, 11, 1601–1604. link ↗
如何引用本页
ScholarGate. (2026, June 3). Online Bagging (Incremental Bootstrap Aggregating). ScholarGate. https://scholargate.app/zh/machine-learning/online-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
- 梯度提升（Gradient Boosting）机器学习↔ compare
- 在线提升 (Online Boosting)机器学习↔ compare
- 随机森林机器学习↔ compare