So sánh phương pháp

	Naive Bayes Mạnh mẽ	Naive Bayes Bán Giám Sát
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2002	2000
Người khởi xướng	Zaffalon, M.	Nigam, K.; McCallum, A. K.; Thrun, S.; Mitchell, T.
Loại	Probabilistic generative classifier with imprecise-probability robustness	Semi-supervised generative classifier
Công trình gốc	Zaffalon, M. (2002). The Naive Credal Classifier. Journal of Statistical Planning and Inference, 105(1), 5–21.	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.
Tên gọi khác	Naive Credal Classifier, NCC, Robust Bayesian Naive Classifier, Imprecise Naive Bayes	SSL Naive Bayes, EM-Naive Bayes, semi-supervised generative classifier, Nigam et al. text classifier
Liên quan	3	4
Tóm tắt	Robust Naive Bayes extends the standard Naive Bayes classifier to handle uncertainty or noise in class-conditional probability estimates by replacing point probability estimates with intervals or sets of distributions. The canonical formulation — the Naive Credal Classifier proposed by Zaffalon (2002) — uses imprecise-probability sets so that predictions are made only when all distributions in the set agree, withholding a label when evidence is insufficient.	Semi-supervised Naive Bayes extends the classic Naive Bayes generative model to exploit large pools of unlabeled data alongside a small labeled set. Using Expectation-Maximization, it iteratively infers soft class assignments for unlabeled examples and re-estimates class and feature parameters, yielding substantially better classifiers when labeled examples are scarce.