Bayes- and Maximum Likelihood Classifiers for Binary Encoded Patterns

Bayes- and Maximum Likelihood Classifiers for Binary Encoded Patterns 

In a binary encoded pattern x each component has the discrete value of 0 or 1. The d-dimensional probability density of a class m is only defined by d probabilities p(x m) with i = 1, 2,. .. d. 

Statistical independence of all components is assumed as for other parametric methods. The overall probability (joint probability) that pattern X belongs to class m is given by the product of the probabilities for all components. 

The logarithm of equation (83) gives a maximum likelihood classifier with a set of decision functions 6 for all classes m (m = 1, 2,. .. H). 

The binapy encoded pattern x (x, Xg x which is classified by w must be augmented by an additional component =1. 

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