Compara mètodes

	Active Learning Boosting	Support Vector Machine d'aprenentatge actiu
Camp	Aprenentatge automàtic	Aprenentatge automàtic
Família	Machine learning	Machine learning
Any d'origen	1998	2001
Autor original	Abe, N. & Mamitsuka, H.	Tong, S. & Koller, D.
Tipus	Hybrid active-learning ensemble	Active learning + kernel classifier
Font seminal	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.	Tong, S., & Koller, D. (2001). Support Vector Machine Active Learning with Applications to Text Classification. Journal of Machine Learning Research, 2, 45–66.
Àlies	boosting-based active learning, query learning with boosting, active boosting, ensemble active learning	Active SVM, AL-SVM, SVM active learning, query-by-committee SVM
Relacionats	4	3
Resum	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.	Active learning SVM combines the strong decision-boundary of support vector machines with an intelligent query strategy that selects the most informative unlabeled instances for human annotation. Introduced by Tong and Koller in 2001, it achieves high classification accuracy using far fewer labeled examples than passive supervised learning, making it practical whenever labeling is expensive or slow.