Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Semi-supervised Logistic Regression× | Régression logistique (ML)× | |
|---|---|---|
| Domaine | Apprentissage automatique | Apprentissage automatique |
| Famille | Machine learning | Machine learning |
| Année d'origine≠ | 1995–2000 | 1958 |
| Auteur d'origine≠ | Nigam, K.; McCallum, A. et al. (EM variant); Yarowsky, D. (self-training) | Cox, D. R. |
| Type≠ | Semi-supervised classifier | Probabilistic linear classifier |
| Source fondatrice≠ | Nigam, K., McCallum, A., Thrun, S., & Mitchell, T. (2000). Text classification from labeled and unlabeled documents using EM. Machine Learning, 39, 103–134. DOI ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| Alias | SSL logistic regression, semi-supervised LR, EM logistic regression, self-training logistic classifier | logit model, logit regression, binomial logistic regression, maximum entropy classifier |
| Apparentées | 5 | 5 |
| Résumé≠ | 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. |
| ScholarGateJeu de données ↗ |
|
|