Linganisha mbinu

	Support Vector Machine yenye Regulareshini	Usajili wa Usawazishaji wa Usawazishaji
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	1995–2004	1996–2005
Mwanzilishi	Cortes, C. & Vapnik, V. (soft-margin SVM); Zhu et al. (L1-SVM)	Tibshirani, R. (lasso); Hoerl & Kennard (ridge); Zou & Hastie (elastic net)
Aina	Regularized discriminative classifier / regressor	Penalized classification model
Chanzo asilia	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.
Majina mbadala	Regularized SVM, L1-SVM, L2-SVM, penalized SVM	penalized logistic regression, L1 logistic regression, L2 logistic regression, elastic net logistic regression
Zinazohusiana	4	5
Muhtasari	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.