手法を比較

	ロバストナイーブベイズ	正則化ナイーブベイズ
分野	機械学習	機械学習
系統	Machine learning	Machine learning
提唱年	2002	1950s–2003
提唱者	Zaffalon, M.	Good, I. J. (Laplace smoothing formalized); Rennie et al. (complement regularization)
種類	Probabilistic generative classifier with imprecise-probability robustness	Probabilistic classifier with regularization
原典	Zaffalon, M. (2002). The Naive Credal Classifier. Journal of Statistical Planning and Inference, 105(1), 5–21.	Rennie, J. D. M., Shih, L., Teevan, J., & Karger, D. R. (2003). Tackling the poor assumptions of Naive Bayes text classifiers. In Proceedings of the 20th International Conference on Machine Learning (ICML-2003), pp. 616–623.
別名	Naive Credal Classifier, NCC, Robust Bayesian Naive Classifier, Imprecise Naive Bayes	Smoothed Naive Bayes, Laplace-smoothed Naive Bayes, Regularized NB, Complement Naive Bayes
関連	3	4
概要	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.	Regularized Naive Bayes augments the classical Naive Bayes probabilistic classifier with explicit smoothing or shrinkage — most commonly Laplace (additive) smoothing — to prevent zero-probability estimates for unseen feature values and to reduce overfitting. The result is a fast, robust classifier that generalizes better than unsmoothed Naive Bayes, particularly on sparse or high-dimensional data such as text.