Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Онлайн-лінійна регресія× | Гребенева регресія× | |
|---|---|---|
| Галузь | Машинне навчання | Машинне навчання |
| Родина | Machine learning | Machine learning |
| Рік появи≠ | 1960 (LMS); 1950 (RLS formalization) | 1970 |
| Автор методу≠ | Widrow, B. & Hoff, M. E. (LMS); Gauss / Plackett (RLS) | Hoerl, A.E. & Kennard, R.W. |
| Тип≠ | Incremental supervised regression | L2-regularized linear regression |
| Основоположне джерело≠ | Shalev-Shwartz, S. (2012). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194. DOI ↗ | Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. DOI ↗ |
| Інші назви | incremental linear regression, streaming linear regression, recursive least squares regression, stochastic gradient descent regression | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| Пов'язані≠ | 6 | 4 |
| Підсумок≠ | Online Linear Regression fits a linear model one observation at a time, updating weights incrementally as each new data point arrives. Unlike batch least-squares, it never needs to store or re-process the full dataset, making it the natural choice for streaming data, very large datasets, and environments where the data-generating process can shift over time. | 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. |
| ScholarGateНабір даних ↗ |
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