Comparer des méthodes

	Empilement semi-supervisé	Propagation d'étiquettes
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2000s–2010s	2002
Auteur d'origine	Combines Wolpert (1992) stacking with semi-supervised learning principles	Zhu, X. & Ghahramani, Z.
Type	Ensemble (stacked generalization with unlabeled data augmentation)	Graph-based semi-supervised classification
Source fondatrice	Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259.	Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.
Alias	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
Apparentées	5	3
Résumé	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.