Machine learningMachine learning
半监督堆叠集成
半监督堆叠集成将经典的堆叠泛化框架扩展到只有一小部分训练样本带有标签的场景。首先在有标签数据上训练基础学习器，然后用它们为无标签样本分配伪标签；扩展后的数据集用于训练更强的基础模型，这些模型的外折预测构成元学习器的输入，从而产生一个利用有标签和无标签结构的两层集成模型。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259. DOI: 10.1016/S0893-6080(05)80023-1 ↗
- Chapelle, O., Schölkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
如何引用本页
ScholarGate. (2026, June 3). Semi-supervised Stacking Ensemble (Self-trained Stacked Generalization). ScholarGate. https://scholargate.app/zh/machine-learning/semi-supervised-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.
- 装袋集成集成学习↔ compare
- 梯度提升（Gradient Boosting）机器学习↔ compare
- 标签传播机器学习↔ compare
- 随机森林机器学习↔ compare
- 堆叠法机器学习↔ compare