Σύγκριση μεθόδων

	Ημι-εποπτευόμενη Μηχανή Υποστήριξης Διανυσμάτων	Λογιστική Παλινδρόμηση
Πεδίο	Μηχανική Μάθηση	Ερευνητική Στατιστική
Οικογένεια	Machine learning	Process / pipeline
Έτος προέλευσης	1999	1958
Δημιουργός	Joachims, T.	David Roxbee Cox
Τύπος	Semi-supervised classifier	Method
Θεμελιώδης πηγή	Joachims, T. (1999). Transductive Inference for Text Classification using Support Vector Machines. Proceedings of the 16th International Conference on Machine Learning (ICML), 200–209.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Εναλλακτικές ονομασίες	S3VM, Transductive SVM, TSVM, Semi-SVM	logit model, binomial logistic regression, LR
Συναφείς	4	3
Σύνοψη	Semi-supervised Support Vector Machine (S3VM) extends the classical SVM by incorporating large quantities of unlabeled data alongside a small labeled training set. It seeks a maximum-margin hyperplane that not only separates the labeled examples but also passes through low-density regions of the full data distribution, yielding better generalization when labeled samples are scarce.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.