Bayes Decision Theory: Discrete Features

A Web Tutorial on Discrete Features of Bayes Decision Theory.

In Pattern Recognition, patterns need to be classified. There are a variety of decision rules (such as Nearest Neighbour) but only Bayes Decision Theory is optimal. Bayes Decision Theory is based on the ever popular Bayes Rule.

In this tutorial, we shall investigate the discrete circumstances, as many times in Pattern Recognition, there is a discrete feature vector that must be classfied. This web tutorial includes examples, formulas and an interactive java applet that will hopefully assist anyone wanting to learn the Bayes Decision Rule for discrete features in Pattern Recognition tasks or any other applicaation.

A web project on Bayes Decision Rule - Discrete Features

in fulfillment of the requirments of

COMP - 644B
Pattern Recogntion

Frank Riggi	Rola Harmouche
Probabilistic Vision Group - CIM	Probabilistic Vision Group - CIM
friggi [at] cim.mcgill.ca	rola.harmouche [at] mail.mcgill.ca

March 2004

Updated: November 2006

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