Salīdzināt metodes

	Tiešsaistes lineārā regresija	Tiešsaistes loģistiskā regresija
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	1960 (LMS); 1950 (RLS formalization)	1960s (perceptron); formalized for logistic loss ~2000s
Autors	Widrow, B. & Hoff, M. E. (LMS); Gauss / Plackett (RLS)	Rosenblatt, F. / Widrow, B. (perceptron era); modern SGD form: Bottou, L.
Tips	Incremental supervised regression	Incremental supervised classifier
Pirmavots	Shalev-Shwartz, S. (2012). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.	Bottou, L. (2010). Large-Scale Machine Learning with Stochastic Gradient Descent. In Proceedings of COMPSTAT 2010, 177–186. Physica-Verlag.
Citi nosaukumi	incremental linear regression, streaming linear regression, recursive least squares regression, stochastic gradient descent regression	incremental logistic regression, streaming logistic regression, SGD logistic classifier, online binary classifier
Saistītās	6	5
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.	Online Logistic Regression fits a logistic classifier one sample (or mini-batch) at a time via stochastic gradient descent, updating model weights as each observation arrives rather than waiting to see the full dataset. This makes it the standard choice for high-volume, streaming, or memory-constrained binary classification problems where batch training is infeasible.