Methoden vergleichen

	Semi-überwachtes Online-Lernen	Online-Lernen
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	2000s–2010s	1958–2000s
Urheber	Goldberg, A.; Li, M.; Zhu, X. (among key contributors)	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
Typ	Hybrid learning paradigm (online + semi-supervised)	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 Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2008), Lecture Notes in Computer Science, 5211, 393–407. Springer.	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.
Aliasnamen	SSOL, online semi-supervised learning, semi-supervised incremental learning, streaming semi-supervised learning	incremental learning, sequential learning, streaming learning, online machine learning
Verwandt	4	6
Zusammenfassung	Semi-supervised Online Learning combines the incremental update style of online learning with the ability to exploit unlabeled examples, enabling models to improve continuously from a data stream in which only a small fraction of arriving instances carry ground-truth labels. It is especially valuable when labeling is expensive or delayed but data arrives in real time.	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.