Võrdle meetodeid

	Võrgu-juhuslik lineaarne regressioon	Veebipõhine õpe
Valdkond	Masinõpe	Masinõpe
Perekond	Machine learning	Machine learning
Tekkeaasta	1960 (LMS); 1950 (RLS formalization)	1958–2000s
Looja	Widrow, B. & Hoff, M. E. (LMS); Gauss / Plackett (RLS)	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
Tüüp	Incremental supervised regression	Learning paradigm (sequential model update)
Algallikas	Shalev-Shwartz, S. (2012). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.
Rööpnimetused	incremental linear regression, streaming linear regression, recursive least squares regression, stochastic gradient descent regression	incremental learning, sequential learning, streaming learning, online machine learning
Seotud	6	6
Kokkuvõte	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.