Сравнение на методи

	Регресия с опорни вектори	Регресия с гребен (Ridge Regression)	Методът на опорните вектори (класификация)
Област	Машинно обучение	Машинно обучение	Машинно обучение
Семейство	Machine learning	Machine learning	Machine learning
Година на възникване	2004	1970	1995
Създател	Smola, A.J. & Schölkopf, B.	Hoerl, A.E. & Kennard, R.W.	Cortes, C. & Vapnik, V.
Тип	Kernel-based supervised model (epsilon-insensitive regression)	L2-regularized linear regression	Maximum-margin classifier (kernel method)
Основополагащ източник	Smola, A.J. & Schölkopf, B. (2004). A Tutorial on Support Vector Regression. Statistics and Computing, 14, 199–222.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.
Други названия	Destek Vektör Regresyonu (SVR), SVR, epsilon-SVR, support vector machine for regression	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
Свързани	4	4	5
Резюме	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.	Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated.	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.