Naive Bayes Classifier Introductory Overview
The Naive Bayes Classifier technique is based on the so-called Bayesian theorem and is particularly suited when the dimensionality of the inputs is high.
Naive Bayes classifier
Bayesian network
Bayesian knowledge base
Case-based reasoning
Decision trees
Inductive logic programming
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Group method of data handling (GMDH)
Learning Automata ...
The naive Bayes classifier is a bayesian learning methods and it is called naive because it incorporates the simplifying assumption that attribute values are conditionally independent, given the classification of the instance.
The most widely used classifiers are the neural network,[106] kernel methods such as the support vector machine,[107] k-nearest neighbor algorithm,[108] Gaussian mixture model,[109] naive Bayes classifier,[110] and decision tree.
Elementary probability and Naive Bayes classifiers. This slide repeats much of the material of the main Probability Slide from Andrew's tutorial series, but this slide-set focusses on disease surveillance examples, ...
 See also: Machine learning, Data mining, Estimation, Support vector machine, Classification
  
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