Comparer des méthodes

	Apprentissage semi-supervisé en ensemble	Boosting
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1998–2005	1990–1997
Auteur d'origine	Blum & Mitchell (co-training); Zhou & Li (tri-training)	Schapire, R. E.; Freund, Y.
Type	Ensemble + semi-supervised hybrid paradigm	Sequential ensemble (iterative reweighting)
Source fondatrice	Zhou, Z.-H., & Li, M. (2005). Tri-training: Exploiting unlabeled data using three classifiers. IEEE Transactions on Knowledge and Data Engineering, 17(11), 1529–1541.	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.
Alias	semi-supervised ensemble, SSL ensemble, ensemble-based SSL, co-training ensemble	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
Apparentées	6	6
Résumé	Ensemble semi-supervised learning combines multiple base learners with the semi-supervised paradigm, exploiting both a small labeled set and a large pool of unlabeled data. By letting diverse classifiers teach each other through pseudo-labeling or co-training, the ensemble improves generalization far beyond what either approach alone could achieve with limited labels.	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.