Machine learning
堆叠法
堆叠法（Stacking），或称堆叠泛化（stacked generalization），是一种集成学习方法，由David Wolpert于1992年提出，它通过一个独立的元模型（Level-1）来组合多个不同基模型（Level-0）的输出。与装袋法（bagging）和提升法（boosting）不同，堆叠法刻意使用异构模型类型，并且是Kaggle竞赛中的标准最终阶段策略。
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来源
- Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259. DOI: 10.1016/S0893-6080(05)80023-1 ↗
- van der Laan, M.J., Polley, E.C. & Hubbard, A.E. (2007). Super Learner. Statistical Applications in Genetics and Molecular Biology, 6(1), Article 25. DOI: 10.2202/1544-6115.1309 ↗
如何引用本页
ScholarGate. (2026, June 1). Stacked Generalization (Stacking Ensemble with a Meta-Learner). ScholarGate. https://scholargate.app/zh/machine-learning/stacking-ensemble
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