Porovnat metody

	Online Learning	Online Random Forest
Obor	Strojové učení	Strojové učení
Rodina	Machine learning	Machine learning
Rok vzniku	1958–2000s	2009
Tvůrce	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)	Saffari, A. et al.
Typ	Learning paradigm (sequential model update)	Incremental ensemble (streaming decision trees)
Původní zdroj	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.	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.
Další názvy	incremental learning, sequential learning, streaming learning, online machine learning	ORF, streaming random forest, incremental random forest, adaptive random forest
Příbuzné	6	6
Shrnutí	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.	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.