Comparer des méthodes

	k-Plus-Proches Voisins Régularisé	Machine à vecteurs de support régularisée
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1967–2000s	1995–2004
Auteur d'origine	Extends Cover & Hart (1967); regularization formulations developed through kernel smoothing literature	Cortes, C. & Vapnik, V. (soft-margin SVM); Zhu et al. (L1-SVM)
Type	Instance-based / lazy learner with regularization	Regularized discriminative classifier / regressor
Source fondatrice	Cover, T. & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27.	Cortes, C. & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.
Alias	regularized kNN, kernel-weighted kNN, distance-regularized nearest neighbors, kNN with regularization	Regularized SVM, L1-SVM, L2-SVM, penalized SVM
Apparentées	4	4
Résumé	Regularized k-Nearest Neighbors (kNN) extends the classical nearest-neighbor algorithm by incorporating regularization mechanisms — most commonly kernel-based distance weighting or bandwidth control — that smooth predictions, reduce sensitivity to the choice of k, and lower variance. The result is a more stable and better-calibrated instance-based learner for classification and regression tasks on tabular data.	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.