Methoden vergleichen

	Online Semi-supervised Learning	Online-Lernen
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	2000s–2010s	1958–2000s
Urheber	Goldberg, A., Li, M., & Zhu, X. (and others in stream learning community)	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
Typ	Incremental / stream-based semi-supervised learning framework	Learning paradigm (sequential model update)
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.	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.
Aliasnamen	stream-based semi-supervised learning, incremental semi-supervised learning, online SSL, semi-supervised online learning	incremental learning, sequential learning, streaming learning, online machine learning
Verwandt	6	6
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.	Online learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight.