Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Онлайн-лінійна регресія× | Онлайн-навчання× | |
|---|---|---|
| Галузь | Машинне навчання | Машинне навчання |
| Родина | Machine learning | Machine learning |
| Рік появи≠ | 1960 (LMS); 1950 (RLS formalization) | 1958–2000s |
| Автор методу≠ | Widrow, B. & Hoff, M. E. (LMS); Gauss / Plackett (RLS) | Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors) |
| Тип≠ | Incremental supervised regression | Learning paradigm (sequential model update) |
| Основоположне джерело≠ | Shalev-Shwartz, S. (2012). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194. DOI ↗ | Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194. DOI ↗ |
| Інші назви | incremental linear regression, streaming linear regression, recursive least squares regression, stochastic gradient descent regression | incremental learning, sequential learning, streaming learning, online machine learning |
| Пов'язані | 6 | 6 |
| 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. | Online learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight. |
| ScholarGateНабір даних ↗ |
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