Methoden vergleichen

	Boosting-Ensemble	AdaBoost
Fachgebiet	Ensemble-Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	1990	1997
Urheber	Robert Schapire	Freund, Y. & Schapire, R.E.
Typ	sequential ensemble	Ensemble (sequential boosting of weak learners)
Wegweisende Quelle	Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5(2), 197-227.	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.
Aliasnamen	adaptive boosting, sequential ensemble	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma
Verwandt	4	5
Zusammenfassung	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.	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.