手法を比較

	オンライン線形回帰	オンライン学習
分野	機械学習	機械学習
系統	Machine learning	Machine learning
提唱年	1960 (LMS); 1950 (RLS formalization)	1958–2000s
提唱者	Widrow, B. & Hoff, M. E. (LMS); Gauss / Plackett (RLS)	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
種類	Incremental supervised regression	Learning paradigm (sequential model update)
原典	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.
別名	incremental linear regression, streaming linear regression, recursive least squares regression, stochastic gradient descent regression	incremental learning, sequential learning, streaming learning, online machine learning
関連	6	6
概要	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.	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.