方法对比
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| 在线线性回归× | 正则化线性回归× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 1960 (LMS); 1950 (RLS formalization) | 1970–2005 |
| 提出者≠ | Widrow, B. & Hoff, M. E. (LMS); Gauss / Plackett (RLS) | Hoerl & Kennard (Ridge, 1970); Tibshirani (Lasso, 1996); Zou & Hastie (Elastic Net, 2005) |
| 类型≠ | Incremental supervised regression | Penalized linear model |
| 开创性文献≠ | Shalev-Shwartz, S. (2012). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194. DOI ↗ | Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ |
| 别名 | incremental linear regression, streaming linear regression, recursive least squares regression, stochastic gradient descent regression | Ridge regression, Lasso regression, Elastic Net regression, penalized regression |
| 相关≠ | 6 | 4 |
| 摘要≠ | 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. | 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. |
| ScholarGate数据集 ↗ |
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