방법 비교

	온라인 로지스틱 회귀	온라인 학습
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	1960s (perceptron); formalized for logistic loss ~2000s	1958–2000s
창시자	Rosenblatt, F. / Widrow, B. (perceptron era); modern SGD form: Bottou, L.	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
유형	Incremental supervised classifier	Learning paradigm (sequential model update)
원전	Bottou, L. (2010). Large-Scale Machine Learning with Stochastic Gradient Descent. In Proceedings of COMPSTAT 2010, 177–186. Physica-Verlag.	Shalev-Shwartz, S. (2011). 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 learning, sequential learning, streaming learning, online machine learning
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 learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight.