So sánh phương pháp

	Naive Bayes Chính quy	Máy học vector hỗ trợ có điều chuẩn (Regularized Support Vector Machine)
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	1950s–2003	1995–2004
Người khởi xướng	Good, I. J. (Laplace smoothing formalized); Rennie et al. (complement regularization)	Cortes, C. & Vapnik, V. (soft-margin SVM); Zhu et al. (L1-SVM)
Loại	Probabilistic classifier with regularization	Regularized discriminative classifier / regressor
Công trình gốc	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.	Cortes, C. & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.
Tên gọi khác	Smoothed Naive Bayes, Laplace-smoothed Naive Bayes, Regularized NB, Complement Naive Bayes	Regularized SVM, L1-SVM, L2-SVM, penalized SVM
Liên quan	4	4
Tóm tắt	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.	Regularized Support Vector Machine extends the classic SVM by explicitly controlling the trade-off between margin maximization and training error through an L1 or L2 penalty parameter. The soft-margin formulation introduced by Cortes and Vapnik in 1995 is itself a regularized model, and later L1-SVM variants additionally promote feature sparsity, enabling automatic variable selection in high-dimensional settings.