השוואת שיטות

	מכונת וקטורים תומכים (סיווג)	Support Vector Regression
תחום	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	1995	2004
הוגה השיטה	Cortes, C. & Vapnik, V.	Smola, A.J. & Schölkopf, B.
סוג	Maximum-margin classifier (kernel method)	Kernel-based supervised model (epsilon-insensitive regression)
מקור מכונן	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.	Smola, A.J. & Schölkopf, B. (2004). A Tutorial on Support Vector Regression. Statistics and Computing, 14, 199–222.
כינויים	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier	Destek Vektör Regresyonu (SVR), SVR, epsilon-SVR, support vector machine for regression
קשורות	5	4
תקציר	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.	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.