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Bayes classifier is popular in pattern recognition because it is an optimal classifier. It is possible to show that the resultant classification minimises the average probability of error. Bayes classifier is based on the assumption that information about classes in the form of prior probabilities and distributions of patterns in the class are known. It employs the posterior probabilities to assign the class label to a test pattern; a pattern is assigned the label of the class that has the maximum posterior probability. The classifier employs Bayes theorem to convert the prior probability into posterior probability based on the pattern to be classified, using the likelihood values. In this chapter, we will introduce some of the important notions associated with the Bayes classifier.
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