So sánh phương pháp

	Học bán giám sát Ensemble	Học bán giám sát
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	1998–2005	1970s–2006 (formalized)
Người khởi xướng	Blum & Mitchell (co-training); Zhou & Li (tri-training)	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Loại	Ensemble + semi-supervised hybrid paradigm	Learning paradigm
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.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Tên gọi khác	semi-supervised ensemble, SSL ensemble, ensemble-based SSL, co-training ensemble	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
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.	Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained.