… workhorses of machine learning: the naive Bayes classifier. … Bayes classifier in the R
programming language. Keywords: Alternative hypothesis, Bayes factor, Bayes factor bound, Bayes…

… Bayesian theory and explore two simple machine learning techniques: a Naive Bayes classifier
and K- Nearest Neighbor Classifier … of these two widely used classifiers. To fulfill these …

… Bayes. While most of the work on naive Bayes compares its performance to other classifiers
on … Also, our current analysis is focused only on the bias of naive Bayes classifier, not on its …

… analysis in medicine from a machine learning perspective: a historical view, … machine
learning in medical diagnosis. In the historical overview, I emphasize the naive Bayesian classifier, …

… machine learning method which uses Naïve Bayes classifier for text data. In Machine Learning
approach, the classifier is built automatically by learning the properties of categories from …

… Classification is a commonly used machine learning and data mining approach.
Depending on the number of target classifications that are used to classify a data set, different …

… optimization models for the naive Bayes classifier where both class probabilities and … Bayes
classifier, tree augmented naive Bayes, the SVM, C4.5 and the nearest neighbor classifier. …

… It describes the process of supervised machine learning and the … Bayes’ theorem on which
the naïve Bayes classifier is based on, and it describes the algorithm for naïve Bayes classifier…

… of values (see table 2) which are treated by Bayesian classifier as 20 discrete values.
Therefore in the Bayesian classifier the data set is split into 20 subsets when approximating …

… Many algorithms have been proposed for the machine learning task of classification. One
of the simplest methods, the naive Bayes classifier, has often been found to give good …