방법 비교

	앙상블 로지스틱 회귀	준지도 학습 로지스틱 회귀
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	1996–2000s	1995–2000
창시자	Breiman, L. (bagging); broader ensemble literature	Nigam, K.; McCallum, A. et al. (EM variant); Yarowsky, D. (self-training)
유형	Ensemble of logistic regression classifiers	Semi-supervised classifier
원전	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.
별칭	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
관련	6	5
요약	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.