Comparer des méthodes

	Bayésien Naïf auto-supervisé	Naive Bayes
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2000	1997
Auteur d'origine	Nigam, K.; McCallum, A. K.; Thrun, S.; Mitchell, T.	Mitchell, T. M. (textbook treatment)
Type	Self-supervised generative classifier	Probabilistic classifier (Bayes' theorem with conditional independence)
Source fondatrice	Nigam, K., McCallum, A. K., Thrun, S., & Mitchell, T. (2000). Text classification from labeled and unlabeled documents using EM. Machine Learning, 39(2-3), 103–134.	Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072
Alias	Self-training Naive Bayes, EM Naive Bayes, Expectation-Maximization Naive Bayes, Pseudo-label Naive Bayes	Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes
Apparentées	5	4
Résumé	Self-supervised Naive Bayes extends the classic Naive Bayes classifier to exploit large pools of unlabeled data by iteratively assigning soft pseudo-labels through an Expectation-Maximization loop. Originally demonstrated for text classification by Nigam et al. (2000), the approach can substantially improve accuracy when labeled examples are scarce but unlabeled data are plentiful.	Naive Bayes is a fast probabilistic classifier that applies Bayes' theorem while assuming that the features are conditionally independent given the class — a method given its standard machine-learning treatment in Tom Mitchell's 1997 textbook Machine Learning. Despite this simplifying ('naive') assumption, it is quick to train and often surprisingly accurate.