Methoden vergleichen

	Regularisierte Online-Lernverfahren	Semi-Supervised Learning
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	2007–2013	1970s–2006 (formalized)
Urheber	Xiao, L.; Shalev-Shwartz, S.; McMahan, H. B. et al.	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Typ	Online optimization framework with regularization	Learning paradigm
Wegweisende Quelle	Xiao, L. (2010). Dual Averaging Methods for Regularized Stochastic and Online Optimization. Journal of Machine Learning Research, 11, 2543–2596.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Aliasnamen	FTRL, Follow-the-Regularized-Leader, online regularized optimization, regularized dual averaging	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Verwandt	6	5
Zusammenfassung	Regularized online learning extends the online learning paradigm by incorporating a regularization penalty into each weight update, controlling model complexity while processing data one example at a time. Algorithms such as Follow-the-Regularized-Leader (FTRL) and Regularized Dual Averaging (RDA) make this approach practical at scale, enabling sparse, well-calibrated models on streaming data.	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.