Сравнение методов

	Регуляризованное онлайн-обучение	Регуляризованная линейная регрессия
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2007–2013	1970–2005
Автор метода	Xiao, L.; Shalev-Shwartz, S.; McMahan, H. B. et al.	Hoerl & Kennard (Ridge, 1970); Tibshirani (Lasso, 1996); Zou & Hastie (Elastic Net, 2005)
Тип	Online optimization framework with regularization	Penalized linear model
Основополагающий источник	Xiao, L. (2010). Dual Averaging Methods for Regularized Stochastic and Online Optimization. Journal of Machine Learning Research, 11, 2543–2596.	Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
Другие названия	FTRL, Follow-the-Regularized-Leader, online regularized optimization, regularized dual averaging	Ridge regression, Lasso regression, Elastic Net regression, penalized regression
Связанные	6	4
Сводка	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.	Regularized linear regression adds a penalty term to the ordinary least-squares objective, shrinking or zeroing out coefficients to reduce overfitting and handle multicollinearity. The three main variants — Ridge (L2 penalty), Lasso (L1 penalty), and Elastic Net (combined L1+L2) — make linear regression usable even when features outnumber observations or predictors are highly correlated.