So sánh phương pháp

	K-Nearest Neighbors Bán Giám Sát	Học bán giám sát
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2002 (semi-supervised extension); 1967 (KNN base)	1970s–2006 (formalized)
Người khởi xướng	Zhu, X. & Ghahramani, Z. (label propagation); Cover, T. & Hart, P. (KNN base)	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Loại	Semi-supervised classifier / label propagation	Learning paradigm
Công trình gốc	Zhu, X. & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Tên gọi khác	SS-KNN, semi-supervised KNN, KNN label propagation, graph-based semi-supervised KNN	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Liên quan	4	5
Tóm tắt	Semi-supervised KNN extends the classic K-nearest neighbors algorithm to exploit large pools of unlabeled data alongside a small labeled set. By building a KNN graph over all observations and propagating known labels through the graph's edges, the method infers labels for unlabeled points without requiring expensive manual annotation of every sample.	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.