So sánh phương pháp

	Học bán giám sát	Voting Ensemble
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	1970s–2006 (formalized)	1990s–2004
Người khởi xướng	Vapnik, V. N. and others (community of researchers, 1970s–2000s)	Lam & Suen; Kuncheva, L. I. (systematic treatment)
Loại	Learning paradigm	Ensemble (combination of multiple classifiers by vote)
Công trình gốc	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8
Tên gọi khác	SSL, semi-supervised machine learning, transductive learning, label-efficient learning	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
Liên quan	5	5
Tóm tắt	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.	A voting ensemble trains several diverse classifiers independently and combines their predictions by a vote: hard voting picks the class chosen by the most models, while soft voting averages their class-probability estimates, optionally with per-model weights. The combination usually outperforms any individual member, and requires no additional training after the base models are fitted.