Compara mètodes

	Regularized Support Vector Machine	Regressió logística regularitzada
Camp	Aprenentatge automàtic	Aprenentatge automàtic
Família	Machine learning	Machine learning
Any d'origen	1995–2004	1996–2005
Autor original	Cortes, C. & Vapnik, V. (soft-margin SVM); Zhu et al. (L1-SVM)	Tibshirani, R. (lasso); Hoerl & Kennard (ridge); Zou & Hastie (elastic net)
Tipus	Regularized discriminative classifier / regressor	Penalized classification model
Font seminal	Cortes, C. & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.	Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
Àlies	Regularized SVM, L1-SVM, L2-SVM, penalized SVM	penalized logistic regression, L1 logistic regression, L2 logistic regression, elastic net logistic regression
Relacionats	4	5
Resum	Regularized Support Vector Machine extends the classic SVM by explicitly controlling the trade-off between margin maximization and training error through an L1 or L2 penalty parameter. The soft-margin formulation introduced by Cortes and Vapnik in 1995 is itself a regularized model, and later L1-SVM variants additionally promote feature sparsity, enabling automatic variable selection in high-dimensional settings.	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.