Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Regresie Liniară Online× | Învățare online× | |
|---|---|---|
| Domeniu | Învățare automată | Învățare automată |
| Familie | Machine learning | Machine learning |
| Anul apariției≠ | 1960 (LMS); 1950 (RLS formalization) | 1958–2000s |
| Autorul original≠ | Widrow, B. & Hoff, M. E. (LMS); Gauss / Plackett (RLS) | Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors) |
| Tip≠ | Incremental supervised regression | Learning paradigm (sequential model update) |
| Sursa seminală≠ | 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 ↗ |
| Denumiri alternative | incremental linear regression, streaming linear regression, recursive least squares regression, stochastic gradient descent regression | incremental learning, sequential learning, streaming learning, online machine learning |
| Înrudite | 6 | 6 |
| Rezumat≠ | 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. |
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