Salīdzināt metodes

	Tiešsaistes loģistiskā regresija	Loģistikas regresija ar daļēji uzraudzītu apmācību
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	1960s (perceptron); formalized for logistic loss ~2000s	1995–2000
Autors	Rosenblatt, F. / Widrow, B. (perceptron era); modern SGD form: Bottou, L.	Nigam, K.; McCallum, A. et al. (EM variant); Yarowsky, D. (self-training)
Tips	Incremental supervised classifier	Semi-supervised classifier
Pirmavots	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.
Citi nosaukumi	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
Saistītās	5	5
Kopsavilkums	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.