方法证据记录
Robust Stacking Ensemble
Robust Stacking Ensemble extends classical stacked generalization by replacing the ordinary meta-learner with a robust estimator — such as a Huber-loss regressor, quantile regression, or a model trained on trimmed residuals — so that the ensemble's combination layer is resistant to outliers and noisy base-learner predictions. It improves predictive accuracy and reliability on real-world datasets with contaminated labels or heavy-tailed error distributions.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Robust Stacking Ensemble (Outlier-Resistant Stacked Generalization)
分类方法记录 · ml-model / machine-learning
- Wolpert, D. H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259. · DOI 10.1016/S0893-6080(05)80023-1
- Ensemble learning. Wikipedia. · URL
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