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在线提升 (Online Boosting)

在线提升将经典的提升框架适配于数据流,一次更新一个弱学习器集成,无需存储完整数据集。Oza-Russell 的公式使用泊松采样实例计数来近似 AdaBoost 的重加权,从而在实时或资源受限的环境中实现准确、自适应的分类。

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来源

  1. Oza, N. C., & Russell, S. (2001). Online Bagging and Boosting. In Artificial Intelligence and Statistics 2001 (pp. 105–112). Morgan Kaufmann. link
  2. Online machine learning. Wikipedia. link

如何引用本页

ScholarGate. (2026, June 3). Online Boosting (Streaming Ensemble Boosting). ScholarGate. https://scholargate.app/zh/machine-learning/online-boosting

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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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被引用于

ScholarGateOnline Boosting (Online Boosting (Streaming Ensemble Boosting)). 于 2026-06-15 检索自 https://scholargate.app/zh/machine-learning/online-boosting · 数据集: https://doi.org/10.5281/zenodo.20539026