Methoden vergleichen

	AdaBoost	Boosting-Ensemble
Fachgebiet	Maschinelles Lernen	Ensemble-Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	1997	1990
Urheber	Freund, Y. & Schapire, R.E.	Robert Schapire
Typ	Ensemble (sequential boosting of weak learners)	sequential ensemble
Wegweisende Quelle	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.	Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5(2), 197-227.
Aliasnamen	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma	adaptive boosting, sequential ensemble
Verwandt	5	4
Zusammenfassung	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.	Boosting is an ensemble method that sequentially trains weak learners and combines them into a strong predictor by focusing on samples that previous models misclassified. Each new weak learner is weighted according to the difficulty of its training task, and final predictions are made via weighted voting. Pioneered by Schapire (1990) and refined in AdaBoost (Freund & Schapire, 1997), boosting converts weak learners (barely better than random) into strong learners through sequential reweighting.