Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Máquina de Vetores de Suporte Bayesiana× | Naive Bayes Bayesiano× | |
|---|---|---|
| Área | Aprendizado de máquina | Aprendizado de máquina |
| Família | Machine learning | Machine learning |
| Ano de origem≠ | 2001–2011 | 1960s (base); Bayesian parameter treatment formalized 2000s |
| Autor original≠ | Polson, N. G. & Scott, S. L.; Tipping, M. E. | Naive Bayes: Maron & Kuhns (1960); full Bayesian treatment formalized by Murphy (2012) and Bishop (2006) |
| Tipo≠ | Bayesian probabilistic classifier / regressor | Probabilistic generative classifier |
| Fonte seminal≠ | Polson, N. G., & Scott, S. L. (2011). Data augmentation for support vector machines. Bayesian Analysis, 6(1), 1–23. DOI ↗ | Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective (Ch. 3, 4). MIT Press. ISBN: 978-0-262-01802-9 |
| Outros nomes | Bayesian SVM, probabilistic SVM, Bayesian kernel machine, BSVM | Bayesian NB, Naive Bayes with Bayesian parameter estimation, Dirichlet-Multinomial Naive Bayes, BNB |
| Relacionados≠ | 3 | 4 |
| Resumo≠ | 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. |
| ScholarGateConjunto de dados ↗ |
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