Salīdzināt metodes

	Tiešsaistes pastiprināšana (Online Boosting)	Daļēji uzraudzīta pastiprināšana
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2001	1999–2009
Autors	Oza, N. C. & Russell, S.	Mallapragada, P. K.; Bennett, K. P.; and others
Tips	Online ensemble (incremental boosting)	Semi-supervised ensemble method
Pirmavots	Oza, N. C., & Russell, S. (2001). Online Bagging and Boosting. In Artificial Intelligence and Statistics 2001 (pp. 105–112). Morgan Kaufmann.	Mallapragada, P. K., Jin, R., Jain, A. K., & Liu, Y. (2009). SemiBoost: Boosting for Semi-supervised Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(11), 2000–2014.
Citi nosaukumi	streaming boosting, incremental boosting, online AdaBoost, online ensemble boosting	SemiBoost, SSL boosting, boosting with unlabeled data, semi-supervised ensemble boosting
Saistītās	6	5
Kopsavilkums	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.	Semi-supervised Boosting is an ensemble learning paradigm that extends classical boosting algorithms — such as AdaBoost — to exploit both labeled and unlabeled data. By propagating label information through a similarity structure over unlabeled instances, it trains stronger classifiers than supervised boosting alone when labeled data are scarce.