So sánh phương pháp

	Máy học vector hỗ trợ Bayes (Bayesian Support Vector Machine)	Naive Bayes Bayesian
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2001–2011	1960s (base); Bayesian parameter treatment formalized 2000s
Người khởi xướng	Polson, N. G. & Scott, S. L.; Tipping, M. E.	Naive Bayes: Maron & Kuhns (1960); full Bayesian treatment formalized by Murphy (2012) and Bishop (2006)
Loại	Bayesian probabilistic classifier / regressor	Probabilistic generative classifier
Công trình gốc	Polson, N. G., & Scott, S. L. (2011). Data augmentation for support vector machines. Bayesian Analysis, 6(1), 1–23.	Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective (Ch. 3, 4). MIT Press. ISBN: 978-0-262-01802-9
Tên gọi khác	Bayesian SVM, probabilistic SVM, Bayesian kernel machine, BSVM	Bayesian NB, Naive Bayes with Bayesian parameter estimation, Dirichlet-Multinomial Naive Bayes, BNB
Liên quan	3	4
Tóm tắt	Bayesian SVM places a prior distribution over the weight vector of a standard SVM and derives a full posterior, enabling calibrated uncertainty estimates, automatic hyperparameter selection, and probabilistic predictions. It combines the strong margin-based geometric intuition of SVMs with the principled uncertainty quantification of Bayesian inference.	Bayesian Naive Bayes applies a fully Bayesian treatment to the parameters of the classic Naive Bayes classifier: instead of estimating class-conditional distributions by maximum likelihood, it places conjugate priors (typically Dirichlet for categorical data or Gaussian-Gamma for continuous data) over the parameters and integrates them out, producing predictive posterior distributions that naturally quantify uncertainty and avoid overfitting on small datasets.