השוואת שיטות

	למידה מקוונת עם רגולריזציה	רגרסיה לוגיסטית עם רגולריזציה
תחום	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	2007–2013	1996–2005
הוגה השיטה	Xiao, L.; Shalev-Shwartz, S.; McMahan, H. B. et al.	Tibshirani, R. (lasso); Hoerl & Kennard (ridge); Zou & Hastie (elastic net)
סוג	Online optimization framework with regularization	Penalized classification model
מקור מכונן	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.
כינויים	FTRL, Follow-the-Regularized-Leader, online regularized optimization, regularized dual averaging	penalized logistic regression, L1 logistic regression, L2 logistic regression, elastic net logistic regression
קשורות	6	5
תקציר	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 logistic regression extends standard logistic regression by adding an L1 (lasso), L2 (ridge), or elastic net penalty to the log-likelihood, shrinking coefficients toward zero and preventing overfitting. It is the default choice for binary or multinomial classification when you want interpretable, sparse, or stable coefficient estimates in high-dimensional or collinear feature spaces.