Naive Bayesian Learning

@misc{citeulike:363910, abstract = {Introduction So-called \"naive\" Bayesian classification is the optimal method of supervised learning if the values of the attributes of an example are independent given the class of the example. Although this assumption is almost always violated in practice, recent work has shown that naive Bayesian learning is remarkably effective in practice and difficult to improve upon systematically [Domingos and Pazzani, 1996] . On many real-world example datasets naive Bayesian learning gives better test ...}, author = {Elkan, Charles }, citeulike-article-id = {363910}, keywords = {mdds, naive\_bayes}, posted-at = {2008-05-17 08:09:52}, priority = {4}, title = {Naive Bayesian Learning}, url = {http://citeseer.ist.psu.edu/30545.html} }

TY - GEN ID - citeulike:363910 L3 - citeulike-article-id:363910 N2 - Introduction So-called "naive" Bayesian classification is the optimal method of supervised learning if the values of the attributes of an example are independent given the class of the example. Although this assumption is almost always violated in practice, recent work has shown that naive Bayesian learning is remarkably effective in practice and difficult to improve upon systematically [Domingos and Pazzani, 1996] . On many real-world example datasets naive Bayesian learning gives better test ... TI - Naive Bayesian Learning KW - mdds KW - naive_bayes AU - Elkan, Charles UR - http://citeseer.ist.psu.edu/30545.html ER -