Linganisha mbinu

	Ensemble Logistic Regression	Semi-supervised Logistic Regression
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	1996–2000s	1995–2000
Mwanzilishi	Breiman, L. (bagging); broader ensemble literature	Nigam, K.; McCallum, A. et al. (EM variant); Yarowsky, D. (self-training)
Aina	Ensemble of logistic regression classifiers	Semi-supervised classifier
Chanzo asilia	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.
Majina mbadala	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
Zinazohusiana	6	5
Muhtasari	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.