Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Support Vector Regression× | Ridge-regressie× | |
|---|---|---|
| Vakgebied | Machine learning | Machine learning |
| Familie | Machine learning | Machine learning |
| Jaar van ontstaan≠ | 2004 | 1970 |
| Grondlegger≠ | Smola, A.J. & Schölkopf, B. | Hoerl, A.E. & Kennard, R.W. |
| Type≠ | Kernel-based supervised model (epsilon-insensitive regression) | L2-regularized linear regression |
| Oorspronkelijke bron≠ | Smola, A.J. & Schölkopf, B. (2004). A Tutorial on Support Vector Regression. Statistics and Computing, 14, 199–222. DOI ↗ | Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. DOI ↗ |
| Aliassen | Destek Vektör Regresyonu (SVR), SVR, epsilon-SVR, support vector machine for regression | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| Verwant | 4 | 4 |
| Samenvatting≠ | 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. |
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