Comparer des méthodes

	Machine à vecteurs de support (Classification)	Plus Proches Voisins (PPV)
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1995	1967
Auteur d'origine	Cortes, C. & Vapnik, V.	Cover, T.M. & Hart, P.E.
Type	Maximum-margin classifier (kernel method)	Instance-based (non-parametric) learning
Source fondatrice	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.	Cover, T.M. & Hart, P.E. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21–27.
Alias	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier	KNN, K-En Yakın Komşu (KNN), nearest neighbor classifier, instance-based learning
Apparentées	5	5
Résumé	The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data.	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.