Compara mètodes

	Aprenentatge automàtic en línia regularitzat	Regressió logística regularitzada
Camp	Aprenentatge automàtic	Aprenentatge automàtic
Família	Machine learning	Machine learning
Any d'origen	2007–2013	1996–2005
Autor original	Xiao, L.; Shalev-Shwartz, S.; McMahan, H. B. et al.	Tibshirani, R. (lasso); Hoerl & Kennard (ridge); Zou & Hastie (elastic net)
Tipus	Online optimization framework with regularization	Penalized classification model
Font seminal	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.
Àlies	FTRL, Follow-the-Regularized-Leader, online regularized optimization, regularized dual averaging	penalized logistic regression, L1 logistic regression, L2 logistic regression, elastic net logistic regression
Relacionats	6	5
Resum	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.