Methoden vergleichen

	Semi-überwachtes Online-Lernen	Aktives Lernen
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	2000s–2010s	2009
Urheber	Goldberg, A.; Li, M.; Zhu, X. (among key contributors)	Burr Settles
Typ	Hybrid learning paradigm (online + semi-supervised)	Interactive supervised learning framework
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.	Settles, B. (2009). Active learning literature survey. University of Wisconsin-Madison Computer Sciences Technical Report 1648.
Aliasnamen	SSOL, online semi-supervised learning, semi-supervised incremental learning, streaming semi-supervised learning	Query Learning, Optimal Experimental Design (ML context), Pool-Based Active Learning, Aktif Öğrenme
Verwandt	4	2
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.	Active learning is an iterative machine-learning paradigm in which a learning algorithm selectively queries an oracle — typically a human annotator — for labels on the most informative unlabeled examples. Formalized by Burr Settles in his seminal 2009 literature survey, active learning addresses the practical bottleneck of annotation cost by achieving high model accuracy with far fewer labeled examples than passive supervised learning requires.