Compară metode

	Mașină cu Vectori de Suport Regularizată	Regresia Lasso
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	1995–2004	1996
Autorul original	Cortes, C. & Vapnik, V. (soft-margin SVM); Zhu et al. (L1-SVM)	Tibshirani, R.
Tip	Regularized discriminative classifier / regressor	Regularized linear regression (L1 penalty)
Sursa seminală	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.
Denumiri alternative	Regularized SVM, L1-SVM, L2-SVM, penalized SVM	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization
Înrudite	4	4
Rezumat	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.	Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter.