Machine learningEnsemble
Boosting Ensemble
Boosting 是一种集成方法，它按顺序训练弱学习器，并通过关注先前模型错误分类的样本来将它们组合成一个强预测器。每个新的弱学习器根据其训练任务的难度进行加权，最终预测通过加权投票做出。Boosting 由 Schapire (1990) 开创，并在 AdaBoost (Freund & Schapire, 1997) 中得到改进，它通过顺序重新加权将弱学习器（仅略好于随机）转换为强学习器。
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来源
- Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5(2), 197-227. DOI: 10.1023/A:1022648800760 ↗
- Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119-139. DOI: 10.1006/jcss.1997.1504 ↗
如何引用本页
ScholarGate. (2026, June 3). Boosting Ensemble Method. ScholarGate. https://scholargate.app/zh/ensemble-learning/boosting-ensemble
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