Σύγκριση μεθόδων

	Online Boosting	Δάσος Τυχαίων Διαδικτυακών Δεδομένων
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2001	2009
Δημιουργός	Oza, N. C. & Russell, S.	Saffari, A. et al.
Τύπος	Online ensemble (incremental boosting)	Incremental ensemble (streaming decision trees)
Θεμελιώδης πηγή	Oza, N. C., & Russell, S. (2001). Online Bagging and Boosting. In Artificial Intelligence and Statistics 2001 (pp. 105–112). Morgan Kaufmann.	Saffari, A., Leistner, C., Santner, J., Godec, M., & Bischof, H. (2009). On-line random forests. In Proceedings of the 3rd IEEE International Workshop on On-Line Learning for Computer Vision (OLCV 2009), pp. 1–8. IEEE.
Εναλλακτικές ονομασίες	streaming boosting, incremental boosting, online AdaBoost, online ensemble boosting	ORF, streaming random forest, incremental random forest, adaptive random forest
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.	Online Random Forest (ORF) extends the classic Random Forest to streaming settings, updating each tree incrementally as new observations arrive without storing or replaying the full training set. Algorithms such as Adaptive Random Forests (ARF) add drift detection so the ensemble adapts when the data distribution changes over time.