Compară metode

	Regresia cu vectori de suport	K-Nearest Neighbors	Mașina cu Vectori Suport (Clasificare)
Domeniu	Învățare automată	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning	Machine learning
Anul apariției	2004	1967	1995
Autorul original	Smola, A.J. & Schölkopf, B.	Cover, T.M. & Hart, P.E.	Cortes, C. & Vapnik, V.
Tip	Kernel-based supervised model (epsilon-insensitive regression)	Instance-based (non-parametric) learning	Maximum-margin classifier (kernel method)
Sursa seminală	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.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.
Denumiri alternative	Destek Vektör Regresyonu (SVR), SVR, epsilon-SVR, support vector machine for regression	KNN, K-En Yakın Komşu (KNN), nearest neighbor classifier, instance-based learning	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
Înrudite	4	5	5
Rezumat	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.	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.