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| Semi-supervised Stacking Ensemble× | Stacking× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2000s–2010s | 1992 |
| Urheber≠ | Combines Wolpert (1992) stacking with semi-supervised learning principles | Wolpert, D.H. |
| Typ≠ | Ensemble (stacked generalization with unlabeled data augmentation) | Ensemble (heterogeneous meta-learning) |
| Wegweisende Quelle≠ | Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259. DOI ↗ | Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259. DOI ↗ |
| Aliasnamen≠ | SSL stacking, semi-supervised stacked generalization, self-trained stacking, semi-supervised meta-learning ensemble | Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | Semi-supervised Stacking Ensemble extends the classic stacked generalization framework to settings where only a fraction of training examples carry labels. Base learners are first trained on labeled data, then used to assign pseudo-labels to unlabeled examples; the expanded dataset trains stronger base models whose out-of-fold predictions form the input to a meta-learner, yielding a two-tier ensemble that exploits both labeled and unlabeled structure. | Stacking, or stacked generalization, is an ensemble method introduced by David Wolpert in 1992 that combines the outputs of several different base models (Level-0) through a separate meta-model (Level-1). Unlike bagging and boosting, it deliberately uses heterogeneous model types, and it is the standard final-stage strategy in Kaggle competitions. |
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