방법 비교

	정규화 나이브 베이즈	정규화 서포트 벡터 머신
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	1950s–2003	1995–2004
창시자	Good, I. J. (Laplace smoothing formalized); Rennie et al. (complement regularization)	Cortes, C. & Vapnik, V. (soft-margin SVM); Zhu et al. (L1-SVM)
유형	Probabilistic classifier with regularization	Regularized discriminative classifier / regressor
원전	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.
별칭	Smoothed Naive Bayes, Laplace-smoothed Naive Bayes, Regularized NB, Complement Naive Bayes	Regularized SVM, L1-SVM, L2-SVM, penalized SVM
관련	4	4
요약	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.