Сравнение методов

	Онлайн-логистическая регрессия	Логистическая регрессия с частичной разметкой
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	1960s (perceptron); formalized for logistic loss ~2000s	1995–2000
Автор метода	Rosenblatt, F. / Widrow, B. (perceptron era); modern SGD form: Bottou, L.	Nigam, K.; McCallum, A. et al. (EM variant); Yarowsky, D. (self-training)
Тип	Incremental supervised classifier	Semi-supervised classifier
Основополагающий источник	Bottou, L. (2010). Large-Scale Machine Learning with Stochastic Gradient Descent. In Proceedings of COMPSTAT 2010, 177–186. Physica-Verlag.	Nigam, K., McCallum, A., Thrun, S., & Mitchell, T. (2000). Text classification from labeled and unlabeled documents using EM. Machine Learning, 39, 103–134.
Другие названия	incremental logistic regression, streaming logistic regression, SGD logistic classifier, online binary classifier	SSL logistic regression, semi-supervised LR, EM logistic regression, self-training logistic classifier
Связанные	5	5
Сводка	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.	Semi-supervised logistic regression extends the standard logistic classifier by incorporating unlabeled data during training. Using self-training, expectation-maximization, or label-propagation wrappers, it iteratively assigns soft labels to unlabeled examples and refines model parameters, improving generalization when labeled data are scarce relative to the full dataset.