Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Регуляризована машина опорних векторів× | Регуляризована лінійна регресія× | |
|---|---|---|
| Галузь | Машинне навчання | Машинне навчання |
| Родина | Machine learning | Machine learning |
| Рік появи≠ | 1995–2004 | 1970–2005 |
| Автор методу≠ | Cortes, C. & Vapnik, V. (soft-margin SVM); Zhu et al. (L1-SVM) | Hoerl & Kennard (Ridge, 1970); Tibshirani (Lasso, 1996); Zou & Hastie (Elastic Net, 2005) |
| Тип≠ | Regularized discriminative classifier / regressor | Penalized linear model |
| Основоположне джерело≠ | 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 ↗ |
| Інші назви | Regularized SVM, L1-SVM, L2-SVM, penalized SVM | Ridge regression, Lasso regression, Elastic Net regression, penalized regression |
| Пов'язані | 4 | 4 |
| Підсумок≠ | 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. | Regularized linear regression adds a penalty term to the ordinary least-squares objective, shrinking or zeroing out coefficients to reduce overfitting and handle multicollinearity. The three main variants — Ridge (L2 penalty), Lasso (L1 penalty), and Elastic Net (combined L1+L2) — make linear regression usable even when features outnumber observations or predictors are highly correlated. |
| ScholarGateНабір даних ↗ |
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