So sánh phương pháp

	Học bán giám sát có điều chuẩn	Học bán giám sát
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2006	1970s–2006 (formalized)
Người khởi xướng	Belkin, M.; Niyogi, P.; Sindhwani, V.	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Loại	Regularized learning paradigm	Learning paradigm
Công trình gốc	Belkin, M., Niyogi, P., & Sindhwani, V. (2006). Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research, 7, 2399–2434.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Tên gọi khác	manifold regularization, graph-regularized SSL, semi-supervised regularization, Laplacian regularization	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Liên quan	6	5
Tóm tắt	Regularized semi-supervised learning adds explicit geometric or graph-based penalty terms to a semi-supervised objective so that the decision function varies smoothly over the data manifold. Pioneered through manifold regularization (Belkin, Niyogi & Sindhwani, 2006), it exploits the structure of both labeled and unlabeled examples to learn more accurate models than supervised regularization alone when labeled data are scarce.	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.