השוואת שיטות

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