Comparer des méthodes

	Ensemble de vote semi-supervisé	Ensemble par vote
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1998–2005	1990s–2004
Auteur d'origine	Zhou, Z.-H. & Li, M. (tri-training); Blum & Mitchell (co-training)	Lam & Suen; Kuncheva, L. I. (systematic treatment)
Type	Semi-supervised ensemble (voting)	Ensemble (combination of multiple classifiers by vote)
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.	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8
Alias	semi-supervised majority vote, SSL voting ensemble, co-training voting classifier, semi-supervised multi-classifier voting	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
Apparentées	5	5
Résumé	A semi-supervised voting ensemble trains multiple classifiers on a small labeled set, then iteratively exploits unlabeled data by having the classifiers label examples they agree on, expanding the training pool until all classifiers vote jointly on test examples. It combines the label-efficiency of semi-supervised learning with the variance-reduction of majority-vote ensembles, making it valuable when annotation is costly.	A voting ensemble trains several diverse classifiers independently and combines their predictions by a vote: hard voting picks the class chosen by the most models, while soft voting averages their class-probability estimates, optionally with per-model weights. The combination usually outperforms any individual member, and requires no additional training after the base models are fitted.