Võrdle meetodeid

	Veebipõhine õpe	Võrgu-juhuslik mets
Valdkond	Masinõpe	Masinõpe
Perekond	Machine learning	Machine learning
Tekkeaasta	1958–2000s	2009
Looja	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)	Saffari, A. et al.
Tüüp	Learning paradigm (sequential model update)	Incremental ensemble (streaming decision trees)
Algallikas	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.
Rööpnimetused	incremental learning, sequential learning, streaming learning, online machine learning	ORF, streaming random forest, incremental random forest, adaptive random forest
Seotud	6	6
Kokkuvõte	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.