Σύγκριση μεθόδων

	Η Λογιστική Παλινδρόμηση Ημι-εποπτευόμενης Μάθησης	Λογιστική Παλινδρόμηση (ML)
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	1995–2000	1958
Δημιουργός	Nigam, K.; McCallum, A. et al. (EM variant); Yarowsky, D. (self-training)	Cox, D. R.
Τύπος	Semi-supervised classifier	Probabilistic linear classifier
Θεμελιώδης πηγή	Nigam, K., McCallum, A., Thrun, S., & Mitchell, T. (2000). Text classification from labeled and unlabeled documents using EM. Machine Learning, 39, 103–134.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Εναλλακτικές ονομασίες	SSL logistic regression, semi-supervised LR, EM logistic regression, self-training logistic classifier	logit model, logit regression, binomial logistic regression, maximum entropy classifier
Συναφείς	5	5
Σύνοψη	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.	Logistic regression is a foundational probabilistic classifier that models the log-odds of a binary (or multinomial) outcome as a linear function of the predictors. Introduced by D. R. Cox in 1958, it remains one of the most widely used and interpretable classification methods in both statistics and machine learning, valued for its calibrated probability outputs and clear coefficient interpretation.