Vertaile menetelmiä

	Online Logistic Regression	Puolivalvottu logistinen regressio
Tieteenala	Koneoppiminen	Koneoppiminen
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	1960s (perceptron); formalized for logistic loss ~2000s	1995–2000
Kehittäjä	Rosenblatt, F. / Widrow, B. (perceptron era); modern SGD form: Bottou, L.	Nigam, K.; McCallum, A. et al. (EM variant); Yarowsky, D. (self-training)
Tyyppi	Incremental supervised classifier	Semi-supervised classifier
Alkuperäislähde	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.
Rinnakkaisnimet	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
Liittyvät	5	5
Tiivistelmä	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.