方法对比
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| 在线线性回归× | 线性回归 (ML)× | |
|---|---|---|
| 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) | 1805–1809 |
| 提出者≠ | Widrow, B. & Hoff, M. E. (LMS); Gauss / Plackett (RLS) | Legendre, A.-M. & Gauss, C.F. |
| 类型≠ | Incremental supervised regression | Supervised regression |
| 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 ↗ | Hastie, T., Tibshirani, R. & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed., Ch. 3). Springer. ISBN: 978-0-387-84858-7 |
| 别名 | incremental linear regression, streaming linear regression, recursive least squares regression, stochastic gradient descent regression | ordinary least squares regression, OLS, least squares regression, multiple linear regression |
| 相关≠ | 6 | 5 |
| 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. | Linear regression fits a straight-line relationship between one or more input features and a continuous numeric outcome by minimising the sum of squared prediction errors. As a machine-learning model it is trained on labeled examples and evaluated on held-out data, making it the simplest supervised learning baseline for any regression task. |
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