Compara mètodes

	Regressió amb Màquines de Vectors de Suport	K-Nearest Neighbors	Màquina de Vectors de Suport (Classificació)
Camp	Aprenentatge automàtic	Aprenentatge automàtic	Aprenentatge automàtic
Família	Machine learning	Machine learning	Machine learning
Any d'origen	2004	1967	1995
Autor original	Smola, A.J. & Schölkopf, B.	Cover, T.M. & Hart, P.E.	Cortes, C. & Vapnik, V.
Tipus	Kernel-based supervised model (epsilon-insensitive regression)	Instance-based (non-parametric) learning	Maximum-margin classifier (kernel method)
Font seminal	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.
Àlies	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
Relacionats	4	5	5
Resum	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.