Comparer des méthodes

	Régression par vecteurs de support	Plus Proches Voisins (PPV)
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2004	1967
Auteur d'origine	Smola, A.J. & Schölkopf, B.	Cover, T.M. & Hart, P.E.
Type	Kernel-based supervised model (epsilon-insensitive regression)	Instance-based (non-parametric) learning
Source fondatrice	Smola, A.J. & Schölkopf, B. (2004). A Tutorial on Support Vector Regression. Statistics and Computing, 14, 199–222.	Cover, T.M. & Hart, P.E. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21–27.
Alias	Destek Vektör Regresyonu (SVR), SVR, epsilon-SVR, support vector machine for regression	KNN, K-En Yakın Komşu (KNN), nearest neighbor classifier, instance-based learning
Apparentées	4	5
Résumé	Support Vector Regression (SVR), described in Smola and Schölkopf's 2004 tutorial, predicts a continuous outcome by fitting a function that stays within an epsilon-wide tube around the data while incurring as little error as possible. It extends the support vector machine idea from classification to regression, using a kernel to capture nonlinear relationships.	K-Nearest Neighbors (KNN), formalized by Cover and Hart in 1967, is a non-parametric, instance-based method that classifies or predicts a new observation by looking at the k closest examples in the training data. For classification it takes a majority vote among those neighbors; for regression it averages their values.