Salīdzināt metodes

	Ensemble Online Learning	Pastiprināšana
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2001	1990–1997
Autors	Oza, N. C. & Russell, S.	Schapire, R. E.; Freund, Y.
Tips	Ensemble (online / incremental)	Sequential ensemble (iterative reweighting)
Pirmavots	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.	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.
Citi nosaukumi	online ensemble methods, streaming ensemble learning, incremental ensemble learning, adaptive ensemble learning	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
Saistītās	6	6
Kopsavilkums	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.	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.