방법 비교

	온라인 부스팅	준지도 학습 부스팅
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	2001	1999–2009
창시자	Oza, N. C. & Russell, S.	Mallapragada, P. K.; Bennett, K. P.; and others
유형	Online ensemble (incremental boosting)	Semi-supervised ensemble method
원전	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.
별칭	streaming boosting, incremental boosting, online AdaBoost, online ensemble boosting	SemiBoost, SSL boosting, boosting with unlabeled data, semi-supervised ensemble boosting
관련	6	5
요약	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.