Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Регуляризоване онлайн-навчання× | Регуляризована лінійна регресія× | |
|---|---|---|
| Галузь | Машинне навчання | Машинне навчання |
| Родина | Machine learning | Machine learning |
| Рік появи≠ | 2007–2013 | 1970–2005 |
| Автор методу≠ | Xiao, L.; Shalev-Shwartz, S.; McMahan, H. B. et al. | Hoerl & Kennard (Ridge, 1970); Tibshirani (Lasso, 1996); Zou & Hastie (Elastic Net, 2005) |
| Тип≠ | Online optimization framework with regularization | Penalized linear model |
| Основоположне джерело≠ | Xiao, L. (2010). Dual Averaging Methods for Regularized Stochastic and Online Optimization. Journal of Machine Learning Research, 11, 2543–2596. link ↗ | 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 | FTRL, Follow-the-Regularized-Leader, online regularized optimization, regularized dual averaging | Ridge regression, Lasso regression, Elastic Net regression, penalized regression |
| Пов'язані≠ | 6 | 4 |
| Підсумок≠ | Regularized online learning extends the online learning paradigm by incorporating a regularization penalty into each weight update, controlling model complexity while processing data one example at a time. Algorithms such as Follow-the-Regularized-Leader (FTRL) and Regularized Dual Averaging (RDA) make this approach practical at scale, enabling sparse, well-calibrated models on streaming data. | 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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