手法を比較

	正則化オンライン学習	正則化線形回帰
分野	機械学習	機械学習
系統	Machine learning	Machine learning
提唱年	2007–2013	1970–2005
提唱者	Xiao, L.; Shalev-Shwartz, S.; McMahan, H. B. et al.	Hoerl & Kennard (Ridge, 1970); Tibshirani (Lasso, 1996); Zou & Hastie (Elastic Net, 2005)
種類	Online optimization framework with regularization	Penalized linear model
原典	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.
別名	FTRL, Follow-the-Regularized-Leader, online regularized optimization, regularized dual averaging	Ridge regression, Lasso regression, Elastic Net regression, penalized regression
関連	6	4
概要	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.