So sánh phương pháp

	Học Trực Tuyến Bayes	Học bán giám sát
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	1990s–2000s	1970s–2006 (formalized)
Người khởi xướng	Opper, M.; Sato, M. (among key contributors)	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Loại	Probabilistic sequential learning	Learning paradigm
Công trình gốc	Opper, M. (1998). A Bayesian approach to on-line learning. In D. Saad (Ed.), On-Line Learning in Neural Networks (pp. 363–378). Cambridge University Press.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Tên gọi khác	online Bayesian inference, sequential Bayesian learning, recursive Bayesian estimation, BOL	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Liên quan	6	5
Tóm tắt	Bayesian online learning applies Bayesian inference sequentially: each time a new observation arrives, the current posterior over model parameters becomes the prior for the next update. The result is a principled probabilistic framework that maintains calibrated uncertainty estimates throughout, making it well-suited for streaming and non-stationary data settings.	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.