방법 비교

	온라인 로지스틱 회귀	온라인 선형 회귀
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	1960s (perceptron); formalized for logistic loss ~2000s	1960 (LMS); 1950 (RLS formalization)
창시자	Rosenblatt, F. / Widrow, B. (perceptron era); modern SGD form: Bottou, L.	Widrow, B. & Hoff, M. E. (LMS); Gauss / Plackett (RLS)
유형	Incremental supervised classifier	Incremental supervised regression
원전	Bottou, L. (2010). Large-Scale Machine Learning with Stochastic Gradient Descent. In Proceedings of COMPSTAT 2010, 177–186. Physica-Verlag.	Shalev-Shwartz, S. (2012). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.
별칭	incremental logistic regression, streaming logistic regression, SGD logistic classifier, online binary classifier	incremental linear regression, streaming linear regression, recursive least squares regression, stochastic gradient descent regression
관련	5	6
요약	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.	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.