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| 준지도 학습 스태킹 앙상블× | 레이블 전파× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2000s–2010s | 2002 |
| 창시자≠ | Combines Wolpert (1992) stacking with semi-supervised learning principles | Zhu, X. & Ghahramani, Z. |
| 유형≠ | Ensemble (stacked generalization with unlabeled data augmentation) | Graph-based semi-supervised classification |
| 원전≠ | Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259. DOI ↗ | Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University. link ↗ |
| 별칭 | SSL stacking, semi-supervised stacked generalization, self-trained stacking, semi-supervised meta-learning ensemble | LP, label spreading, graph-based semi-supervised learning, harmonic label propagation |
| 관련≠ | 5 | 3 |
| 요약≠ | 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. | Label Propagation is a graph-based semi-supervised learning algorithm introduced by Zhu and Ghahramani in 2002 that spreads class labels from a small set of labeled nodes to a large set of unlabeled nodes by iteratively diffusing label information along the edges of a similarity graph, exploiting the manifold structure of the data. |
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