Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Regresia Ridge× | Mașina cu Vectori Suport (Clasificare)× | |
|---|---|---|
| Domeniu | Învățare automată | Învățare automată |
| Familie | Machine learning | Machine learning |
| Anul apariției≠ | 1970 | 1995 |
| Autorul original≠ | Hoerl, A.E. & Kennard, R.W. | Cortes, C. & Vapnik, V. |
| Tip≠ | L2-regularized linear regression | Maximum-margin classifier (kernel method) |
| Sursa seminală≠ | Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. DOI ↗ | Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297. DOI ↗ |
| Denumiri alternative | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization | Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier |
| Înrudite≠ | 4 | 5 |
| Rezumat≠ | 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. |
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