Machine learning
AdaBoost
AdaBoost（自适应增强）是最初的增强算法，由 Yoav Freund 和 Robert Schapire 于 1997 年提出，它通过给予错误分类的观测值更多权重来组合一系列简单的弱学习器。作为梯度增强的先驱，它简单、可解释，并且是分类任务的强大基线。
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来源
- 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 1). AdaBoost (Adaptive Boosting). ScholarGate. https://scholargate.app/zh/machine-learning/adaboost
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