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	Uczenie online z regularyzacją	Uczenie online
Dziedzina	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Machine learning	Machine learning
Rok powstania	2007–2013	1958–2000s
Twórca	Xiao, L.; Shalev-Shwartz, S.; McMahan, H. B. et al.	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
Typ	Online optimization framework with regularization	Learning paradigm (sequential model update)
Źródło pierwotne	Xiao, L. (2010). Dual Averaging Methods for Regularized Stochastic and Online Optimization. Journal of Machine Learning Research, 11, 2543–2596.	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.
Inne nazwy	FTRL, Follow-the-Regularized-Leader, online regularized optimization, regularized dual averaging	incremental learning, sequential learning, streaming learning, online machine learning
Pokrewne	6	6
Podsumowanie	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.	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.