Сравнение методов

	Ансамблевая логистическая регрессия	Логистическая регрессия с частичной разметкой
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	1996–2000s	1995–2000
Автор метода	Breiman, L. (bagging); broader ensemble literature	Nigam, K.; McCallum, A. et al. (EM variant); Yarowsky, D. (self-training)
Тип	Ensemble of logistic regression classifiers	Semi-supervised classifier
Основополагающий источник	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.	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	logistic regression ensemble, bagged logistic regression, aggregated logistic regression, logistic ensemble classifier	SSL logistic regression, semi-supervised LR, EM logistic regression, self-training logistic classifier
Связанные	6	5
Сводка	Ensemble Logistic Regression trains multiple logistic regression classifiers on varied subsets or perturbations of the training data and combines their probability estimates by averaging or voting. The approach preserves logistic regression's probabilistic interpretability while reducing variance and improving predictive stability through aggregation.	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.