Machine learningMachine learning
正则化堆叠集成
正则化堆叠集成（Regularized Stacking Ensemble）是一种两级集成方法，它通过正则化的元学习器（通常是岭回归、Lasso或弹性网络）组合来自多个不同基础学习器的预测，以抑制组合层中的过拟合。正则化确保元学习器为基础模型的输出分配稳定、校准良好的权重，而不是记忆训练折叠预测中的噪声。
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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 ↗
- Breiman, L. (1996). Stacked Regressions. Machine Learning, 24(1), 49–64. DOI: 10.1007/BF00117832 ↗
如何引用本页
ScholarGate. (2026, June 3). Regularized Stacking Ensemble (Stacked Generalization with Regularized Meta-Learner). ScholarGate. https://scholargate.app/zh/machine-learning/regularized-stacking-ensemble
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.
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