方法对比

	在线线性回归	在线逻辑回归
领域	机器学习	机器学习
方法族	Machine learning	Machine learning
起源年份	1960 (LMS); 1950 (RLS formalization)	1960s (perceptron); formalized for logistic loss ~2000s
提出者	Widrow, B. & Hoff, M. E. (LMS); Gauss / Plackett (RLS)	Rosenblatt, F. / Widrow, B. (perceptron era); modern SGD form: Bottou, L.
类型	Incremental supervised regression	Incremental supervised classifier
开创性文献	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.
别名	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
相关	6	5
摘要	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.