Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Regularizēta atbalsta vektoru mašīna× | LASSO regresija× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 1995–2004 | 1996 |
| Autors≠ | Cortes, C. & Vapnik, V. (soft-margin SVM); Zhu et al. (L1-SVM) | Tibshirani, R. |
| Tips≠ | Regularized discriminative classifier / regressor | Regularized linear regression (L1 penalty) |
| Pirmavots≠ | Cortes, C. & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. DOI ↗ | Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ |
| Citi nosaukumi | Regularized SVM, L1-SVM, L2-SVM, penalized SVM | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization |
| Saistītās | 4 | 4 |
| Kopsavilkums≠ | Regularized Support Vector Machine extends the classic SVM by explicitly controlling the trade-off between margin maximization and training error through an L1 or L2 penalty parameter. The soft-margin formulation introduced by Cortes and Vapnik in 1995 is itself a regularized model, and later L1-SVM variants additionally promote feature sparsity, enabling automatic variable selection in high-dimensional settings. | 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. |
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