השוואת שיטות

	מכונת וקטורים תומכים מווסתת (Regularized Support Vector Machine)	רגרסיה לוגיסטית עם רגולריזציה
תחום	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	1995–2004	1996–2005
הוגה השיטה	Cortes, C. & Vapnik, V. (soft-margin SVM); Zhu et al. (L1-SVM)	Tibshirani, R. (lasso); Hoerl & Kennard (ridge); Zou & Hastie (elastic net)
סוג	Regularized discriminative classifier / regressor	Penalized classification model
מקור מכונן	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.
כינויים	Regularized SVM, L1-SVM, L2-SVM, penalized SVM	penalized logistic regression, L1 logistic regression, L2 logistic regression, elastic net logistic regression
קשורות	4	5
תקציר	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.