Methoden vergleichen

	Online Semi-supervised Learning	Semi-Supervised Learning
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	2000s–2010s	1970s–2006 (formalized)
Urheber	Goldberg, A., Li, M., & Zhu, X. (and others in stream learning community)	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Typ	Incremental / stream-based semi-supervised learning framework	Learning paradigm
Wegweisende Quelle	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
Aliasnamen	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
Verwandt	6	5
Zusammenfassung	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.