So sánh phương pháp

	Gaussian Process Bán Giám Sát	Học bán giám sát
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2004	1970s–2006 (formalized)
Người khởi xướng	Lawrence, N. D. & Jordan, M. I.	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Loại	Probabilistic model (semi-supervised)	Learning paradigm
Công trình gốc	Lawrence, N. D., & Jordan, M. I. (2004). Semi-supervised learning via Gaussian processes. In Advances in Neural Information Processing Systems (NIPS), 17, 753–760. MIT Press.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Tên gọi khác	SS-GP, semi-supervised GP, Gaussian process with unlabeled data, GP manifold learning	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Liên quan	5	5
Tóm tắt	Semi-supervised Gaussian Process extends the probabilistic GP framework to exploit unlabeled data alongside a small set of labeled observations. By placing a GP prior over functions and leveraging the geometric structure revealed by unlabeled inputs, it learns more accurate and better-calibrated predictors than a purely supervised GP when labels are scarce, making it well suited for scientific and medical problems where annotation is expensive.	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.