قارن الطرق

	الانحدار الخطي عبر الإنترنت	الانحدار الخطي المنتظم
المجال	تعلم الآلة	تعلم الآلة
العائلة	Machine learning	Machine learning
سنة النشأة	1960 (LMS); 1950 (RLS formalization)	1970–2005
صاحب الطريقة	Widrow, B. & Hoff, M. E. (LMS); Gauss / Plackett (RLS)	Hoerl & Kennard (Ridge, 1970); Tibshirani (Lasso, 1996); Zou & Hastie (Elastic Net, 2005)
النوع	Incremental supervised regression	Penalized linear model
المصدر التأسيسي	Shalev-Shwartz, S. (2012). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.	Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
الأسماء البديلة	incremental linear regression, streaming linear regression, recursive least squares regression, stochastic gradient descent regression	Ridge regression, Lasso regression, Elastic Net regression, penalized regression
ذات صلة	6	4
الملخص	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.	Regularized linear regression adds a penalty term to the ordinary least-squares objective, shrinking or zeroing out coefficients to reduce overfitting and handle multicollinearity. The three main variants — Ridge (L2 penalty), Lasso (L1 penalty), and Elastic Net (combined L1+L2) — make linear regression usable even when features outnumber observations or predictors are highly correlated.