Comparer des méthodes

	Boost par apprentissage semi-supervisé	AdaBoost
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1999–2009	1997
Auteur d'origine	Mallapragada, P. K.; Bennett, K. P.; and others	Freund, Y. & Schapire, R.E.
Type	Semi-supervised ensemble method	Ensemble (sequential boosting of weak learners)
Source fondatrice	Mallapragada, P. K., Jin, R., Jain, A. K., & Liu, Y. (2009). SemiBoost: Boosting for Semi-supervised Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(11), 2000–2014.	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	SemiBoost, SSL boosting, boosting with unlabeled data, semi-supervised ensemble boosting	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma
Apparentées	5	5
Résumé	Semi-supervised Boosting is an ensemble learning paradigm that extends classical boosting algorithms — such as AdaBoost — to exploit both labeled and unlabeled data. By propagating label information through a similarity structure over unlabeled instances, it trains stronger classifiers than supervised boosting alone when labeled data are scarce.	AdaBoost (Adaptive Boosting) is the original boosting algorithm, introduced by Yoav Freund and Robert Schapire in 1997, that combines a sequence of simple weak learners by giving more weight to the observations they get wrong. The forerunner of gradient boosting, it is simple, interpretable, and a strong baseline for classification.