방법 비교

	능동 학습 부스팅	온라인 부스팅
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	1998	2001
창시자	Abe, N. & Mamitsuka, H.	Oza, N. C. & Russell, S.
유형	Hybrid active-learning ensemble	Online ensemble (incremental boosting)
원전	Abe, N. & Mamitsuka, H. (1998). Query Learning Strategies Using Boosting and Bagging. Proceedings of the 15th International Conference on Machine Learning (ICML 1998), pp. 1–9. Morgan Kaufmann.	Oza, N. C., & Russell, S. (2001). Online Bagging and Boosting. In Artificial Intelligence and Statistics 2001 (pp. 105–112). Morgan Kaufmann.
별칭	boosting-based active learning, query learning with boosting, active boosting, ensemble active learning	streaming boosting, incremental boosting, online AdaBoost, online ensemble boosting
관련	4	6
요약	Active Learning Boosting combines the query-driven label acquisition of active learning with the weighted-ensemble logic of boosting algorithms such as AdaBoost. The model iteratively selects the most informative unlabeled examples to annotate — guided by the disagreement or uncertainty within the boosting ensemble — and retrains after each new label, achieving high accuracy with far fewer labeled examples than passive learning.	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.