Compara mètodes

	Naive Bayes bayesià	Naive Bayes semi-supervisat
Camp	Aprenentatge automàtic	Aprenentatge automàtic
Família	Machine learning	Machine learning
Any d'origen	1960s (base); Bayesian parameter treatment formalized 2000s	2000
Autor original	Naive Bayes: Maron & Kuhns (1960); full Bayesian treatment formalized by Murphy (2012) and Bishop (2006)	Nigam, K.; McCallum, A. K.; Thrun, S.; Mitchell, T.
Tipus	Probabilistic generative classifier	Semi-supervised generative classifier
Font seminal	Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective (Ch. 3, 4). MIT Press. ISBN: 978-0-262-01802-9	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.
Àlies	Bayesian NB, Naive Bayes with Bayesian parameter estimation, Dirichlet-Multinomial Naive Bayes, BNB	SSL Naive Bayes, EM-Naive Bayes, semi-supervised generative classifier, Nigam et al. text classifier
Relacionats	4	4
Resum	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.	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.