Salīdzināt metodes

	Tiešsaistes lineārā regresija	Regulētā lineārā regresija (Ridge Regression)
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	1960 (LMS); 1950 (RLS formalization)	1970
Autors	Widrow, B. & Hoff, M. E. (LMS); Gauss / Plackett (RLS)	Hoerl, A.E. & Kennard, R.W.
Tips	Incremental supervised regression	L2-regularized linear regression
Pirmavots	Shalev-Shwartz, S. (2012). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Citi nosaukumi	incremental linear regression, streaming linear regression, recursive least squares regression, stochastic gradient descent regression	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Saistītās	6	4
Kopsavilkums	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.	Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated.