方法证据记录
Stacked Generalization
Stacked generalization, or stacking, is a two-level ensemble method where base-level classifiers are trained on the original data, and a meta-learner is trained on the predictions of the base classifiers. The meta-learner learns how to best combine base predictions rather than using fixed aggregation rules. Introduced by David Wolpert in 1992, stacking achieves state-of-the-art performance by automatically learning the optimal weighting and interaction patterns among base models.
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。
Stacked Generalization Ensemble
分类方法记录 · ml-model / ensemble-learning
- 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 10.1023/a:1018046112532
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