Σύγκριση μεθόδων

	Online Boosting	Ενίσχυση
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2001	1990–1997
Δημιουργός	Oza, N. C. & Russell, S.	Schapire, R. E.; Freund, Y.
Τύπος	Online ensemble (incremental boosting)	Sequential ensemble (iterative reweighting)
Θεμελιώδης πηγή	Oza, N. C., & Russell, S. (2001). Online Bagging and Boosting. In Artificial Intelligence and Statistics 2001 (pp. 105–112). Morgan Kaufmann.	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.
Εναλλακτικές ονομασίες	streaming boosting, incremental boosting, online AdaBoost, online ensemble boosting	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
Συναφείς	6	6
Σύνοψη	Online Boosting adapts the classical boosting framework to data streams, updating an ensemble of weak learners one example at a time without storing the full dataset. The Oza-Russell formulation approximates AdaBoost's reweighting using Poisson-sampled instance counts, enabling accurate, adaptive classification in real-time or resource-constrained environments.	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.