Comparer des méthodes

	AdaBoost	Vote majoritaire
Domaine	Apprentissage automatique	Apprentissage ensembliste
Famille	Machine learning	Machine learning
Année d'origine	1997	1996
Auteur d'origine	Freund, Y. & Schapire, R.E.	Leo Breiman
Type	Ensemble (sequential boosting of weak learners)	voting aggregation
Source fondatrice	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.	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.
Alias	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma	hard voting
Apparentées	5	5
Résumé	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.	Majority voting is an ensemble method that combines predictions from multiple base classifiers by selecting the class that receives the most votes. Each base classifier casts one vote for a predicted class, and the final prediction is the class with the majority (plurality). This approach was formalized by Leo Breiman and colleagues in the 1990s as a simple yet effective way to improve classification accuracy.