Methoden vergelijken

	Ensemble Online Learning	Online Leren	Random Forest
Vakgebied	Machine learning	Machine learning	Machine learning
Familie	Machine learning	Machine learning	Machine learning
Jaar van ontstaan	2001	1958–2000s	2001
Grondlegger	Oza, N. C. & Russell, S.	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)	Breiman, L.
Type	Ensemble (online / incremental)	Learning paradigm (sequential model update)	Ensemble (bagging of decision trees)
Oorspronkelijke bron	Oza, N. C., & Russell, S. (2001). Online bagging and boosting. In Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics (AISTATS 2001), pp. 229–236.	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Aliassen	online ensemble methods, streaming ensemble learning, incremental ensemble learning, adaptive ensemble learning	incremental learning, sequential learning, streaming learning, online machine learning	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Verwant	6	6	4
Samenvatting	Ensemble Online Learning combines multiple base learners that are trained incrementally on a stream of data, updating each model one observation at a time. By aggregating the predictions of diverse online learners, the ensemble achieves accuracy and robustness that surpass any single incremental model, while adapting continuously to changing data distributions.	Online learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.