From f23321cf5f343d541bed9418654c1c1c2c64ba06 Mon Sep 17 00:00:00 2001 From: norangebit Date: Sat, 5 Jun 2021 18:12:53 +0200 Subject: [PATCH] Update bibliography --- bibliography.bib | 1 + 1 file changed, 1 insertion(+) diff --git a/bibliography.bib b/bibliography.bib index 8dc56eb..ad0e342 100644 --- a/bibliography.bib +++ b/bibliography.bib @@ -7,6 +7,7 @@ urldate = {2021-06-03}, abstract = {In statistics, naive Bayes classifiers are a family of simple "probabilistic classifiers" based on applying Bayes' theorem with strong (na\"ive) independence assumptions between the features (see Bayes classifier). They are among the simplest Bayesian network models, but coupled with kernel density estimation, they can achieve higher accuracy levels.Na\"ive Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of variables (features/predictors) in a learning problem. Maximum-likelihood training can be done by evaluating a closed-form expression, which takes linear time, rather than by expensive iterative approximation as used for many other types of classifiers. In the statistics and computer science literature, naive Bayes models are known under a variety of names, including simple Bayes and independence Bayes. All these names reference the use of Bayes' theorem in the classifier's decision rule, but na\"ive Bayes is not (necessarily) a Bayesian method.}, annotation = {Page Version ID: 1024247473}, + file = {/home/norangebit/Documenti/10-personal/12-organizzation/06-zotero/storage/5T4T73X4/index.html}, langid = {english} }