방법 비교

	릿지 회귀(Ridge Regression)	서포트 벡터 머신 (분류)
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	1970	1995
창시자	Hoerl, A.E. & Kennard, R.W.	Cortes, C. & Vapnik, V.
유형	L2-regularized linear regression	Maximum-margin classifier (kernel method)
원전	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.
별칭	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
관련	4	5
요약	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.