השוואת שיטות

	Active Learning Voting Ensemble	שק (Bootstrap Aggregating)	בוסטינג
תחום	למידת מכונה	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning	Machine learning
שנת המקור	1992	1996	1990–1997
הוגה השיטה	Seung, H. S., Opper, M., & Sompolinsky, H.	Breiman, L.	Schapire, R. E.; Freund, Y.
סוג	Active learning with ensemble voting	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Sequential ensemble (iterative reweighting)
מקור מכונן	Seung, H. S., Opper, M., & Sompolinsky, H. (1992). Query by committee. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory (COLT '92), pp. 287–294. ACM.	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.
כינויים	Query by Committee, QBC, active ensemble learning, committee-based active learning	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
קשורות	5	5	6
תקציר	Active Learning Voting Ensemble — formally known as Query by Committee — is an active learning strategy that trains a committee of diverse models and selects the unlabeled examples where the committee members disagree most for human annotation. By focusing labeling effort on the most informative points, it achieves high accuracy with far fewer labeled examples than passive learning requires.	Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner.	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.