So sánh phương pháp

	Học bán giám sát trực tuyến	Học bán giám sát
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2000s–2010s	1970s–2006 (formalized)
Người khởi xướng	Goldberg, A., Li, M., & Zhu, X. (and others in stream learning community)	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Loại	Incremental / stream-based semi-supervised learning framework	Learning paradigm
Công trình gốc	Goldberg, A., Li, M., & Zhu, X. (2008). Online manifold regularization: A new learning setting and empirical study. In Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), pp. 393–407. Springer.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Tên gọi khác	stream-based semi-supervised learning, incremental semi-supervised learning, online SSL, semi-supervised online learning	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Liên quan	6	5
Tóm tắt	Online semi-supervised learning combines the incremental, one-pass nature of online learning with the ability to exploit unlabeled data alongside sparse labeled observations. It is designed for settings where data arrives as a stream and obtaining labels for every instance is expensive or impractical — such as real-time classification of web content, sensor readings, or social media posts.	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.