Σύγκριση μεθόδων

	Γραμμική Παλινδρόμηση Online	Γραμμική Παλινδρόμηση με Κανονικοποίηση
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	1960 (LMS); 1950 (RLS formalization)	1970–2005
Δημιουργός	Widrow, B. & Hoff, M. E. (LMS); Gauss / Plackett (RLS)	Hoerl & Kennard (Ridge, 1970); Tibshirani (Lasso, 1996); Zou & Hastie (Elastic Net, 2005)
Τύπος	Incremental supervised regression	Penalized linear model
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	incremental linear regression, streaming linear regression, recursive least squares regression, stochastic gradient descent regression	Ridge regression, Lasso regression, Elastic Net regression, penalized regression
Συναφείς	6	4
Σύνοψη	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.