Sammenlign metoder

	Online Boosting	Semi-supervised Boosting
Fagområde	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Oprindelsesår	2001	1999–2009
Ophavsperson	Oza, N. C. & Russell, S.	Mallapragada, P. K.; Bennett, K. P.; and others
Type	Online ensemble (incremental boosting)	Semi-supervised ensemble method
Oprindelig kilde	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.
Aliasser	streaming boosting, incremental boosting, online AdaBoost, online ensemble boosting	SemiBoost, SSL boosting, boosting with unlabeled data, semi-supervised ensemble boosting
Relaterede	6	5
Resumé	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.