Sammenlign metoder

	Online Lineær Regression	Online læring
Fagområde	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Oprindelsesår	1960 (LMS); 1950 (RLS formalization)	1958–2000s
Ophavsperson	Widrow, B. & Hoff, M. E. (LMS); Gauss / Plackett (RLS)	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
Type	Incremental supervised regression	Learning paradigm (sequential model update)
Oprindelig kilde	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.
Aliasser	incremental linear regression, streaming linear regression, recursive least squares regression, stochastic gradient descent regression	incremental learning, sequential learning, streaming learning, online machine learning
Relaterede	6	6
Resumé	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.