Porovnat metody

	Online Logistic Regression	Logistická regrese s částečným dohledem
Obor	Strojové učení	Strojové učení
Rodina	Machine learning	Machine learning
Rok vzniku	1960s (perceptron); formalized for logistic loss ~2000s	1995–2000
Tvůrce	Rosenblatt, F. / Widrow, B. (perceptron era); modern SGD form: Bottou, L.	Nigam, K.; McCallum, A. et al. (EM variant); Yarowsky, D. (self-training)
Typ	Incremental supervised classifier	Semi-supervised classifier
Původní zdroj	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.
Další názvy	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
Příbuzné	5	5
Shrnutí	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.