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| Lasso-regresszió× | Ridge Regression× | Support Vector Machine (Osztályozás)× | |
|---|---|---|---|
| Tudományterület | Gépi tanulás | Gépi tanulás | Gépi tanulás |
| Módszercsalád | Machine learning | Machine learning | Machine learning |
| Keletkezés éve≠ | 1996 | 1970 | 1995 |
| Megalkotó≠ | Tibshirani, R. | Hoerl, A.E. & Kennard, R.W. | Cortes, C. & Vapnik, V. |
| Típus≠ | Regularized linear regression (L1 penalty) | L2-regularized linear regression | Maximum-margin classifier (kernel method) |
| Alapmű≠ | Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ | 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 ↗ |
| Alternatív nevek | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization | Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier |
| Kapcsolódó≠ | 4 | 4 | 5 |
| Összefoglaló≠ | Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter. | 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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