So sánh phương pháp

	Hồi quy véc-tơ hỗ trợ	Ridge Regression	Máy Vectơ Hỗ trợ (Phân loại)
Lĩnh vực	Học máy	Học máy	Học máy
Họ	Machine learning	Machine learning	Machine learning
Năm ra đời	2004	1970	1995
Người khởi xướng	Smola, A.J. & Schölkopf, B.	Hoerl, A.E. & Kennard, R.W.	Cortes, C. & Vapnik, V.
Loại	Kernel-based supervised model (epsilon-insensitive regression)	L2-regularized linear regression	Maximum-margin classifier (kernel method)
Công trình gốc	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.
Tên gọi khác	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
Liên quan	4	4	5
Tóm tắt	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.