Сравнение на методи

	Мнозинствено гласуване	AdaBoost	Бустинг Ансамбъл
Област	Ансамблово обучение	Машинно обучение	Ансамблово обучение
Семейство	Machine learning	Machine learning	Machine learning
Година на възникване	1996	1997	1990
Създател	Leo Breiman	Freund, Y. & Schapire, R.E.	Robert Schapire
Тип	voting aggregation	Ensemble (sequential boosting of weak learners)	sequential ensemble
Основополагащ източник	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.	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.
Други названия	hard voting	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma	adaptive boosting, sequential ensemble
Свързани	5	5	4
Резюме	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.	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.