Salīdzināt metodes

	Tiešsaistes aktīvā mācīšanās	Tiešsaistes nejaušais mežs
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2000s	2009
Autors	Cesa-Bianchi, N. and others (multiple contributors)	Saffari, A. et al.
Tips	Hybrid learning paradigm (online + active)	Incremental ensemble (streaming decision trees)
Pirmavots	Cesa-Bianchi, N., Gentile, C., & Zaniboni, L. (2006). Worst-case analysis of selective sampling for linear classification. Journal of Machine Learning Research, 7, 1205–1230.	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.
Citi nosaukumi	streaming active learning, online query-by-committee, sequential active learning, incremental active learning	ORF, streaming random forest, incremental random forest, adaptive random forest
Saistītās	6	6
Kopsavilkums	Online active learning combines two complementary paradigms: it processes data as a stream (online learning) and selectively requests labels only for the most informative instances (active learning). The result is a model that adapts continuously to new data while keeping labeling costs low — useful whenever labeled data is expensive and examples arrive sequentially rather than all at once.	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.