So sánh phương pháp

	Học bán giám sát Ensemble	Bagging (Bootstrap Aggregating)
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	1998–2005	1996
Người khởi xướng	Blum & Mitchell (co-training); Zhou & Li (tri-training)	Breiman, L.
Loại	Ensemble + semi-supervised hybrid paradigm	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)
Công trình gốc	Zhou, Z.-H., & Li, M. (2005). Tri-training: Exploiting unlabeled data using three classifiers. IEEE Transactions on Knowledge and Data Engineering, 17(11), 1529–1541.	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.
Tên gọi khác	semi-supervised ensemble, SSL ensemble, ensemble-based SSL, co-training ensemble	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor
Liên quan	6	5
Tóm tắt	Ensemble semi-supervised learning combines multiple base learners with the semi-supervised paradigm, exploiting both a small labeled set and a large pool of unlabeled data. By letting diverse classifiers teach each other through pseudo-labeling or co-training, the ensemble improves generalization far beyond what either approach alone could achieve with limited labels.	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.