Sammenlign metoder

	Ensemble semi-superviseret læring	Bagging (Bootstrap Aggregating)
Fagområde	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Oprindelsesår	1998–2005	1996
Ophavsperson	Blum & Mitchell (co-training); Zhou & Li (tri-training)	Breiman, L.
Type	Ensemble + semi-supervised hybrid paradigm	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)
Oprindelig kilde	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.
Aliasser	semi-supervised ensemble, SSL ensemble, ensemble-based SSL, co-training ensemble	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor
Relaterede	6	5
Resumé	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.