방법 비교

	Robust Naive Bayes	준지도 학습 나이브 베이즈
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	2002	2000
창시자	Zaffalon, M.	Nigam, K.; McCallum, A. K.; Thrun, S.; Mitchell, T.
유형	Probabilistic generative classifier with imprecise-probability robustness	Semi-supervised generative classifier
원전	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.
별칭	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
관련	3	4
요약	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.