Understanding the gaussian distribution (First case)

This morning I started working on recording the steps necessary to use a gaussian distribution. Most of what was
accomplished today was simply reading and understanding how to implement the discriminant functions.

I read the Duda, Stork and Hart book: "Pattern Classification 2nd edition" (section 2.6). This section describes the
three cases for normal distribution. After reading through the first case I created a document outlining what was needed
to create the function. I'll use this document to work from in creating the discriminant functions in Matlab.

The idea is to create a discriminant function for each emotion category. Each face vector will be passed to each of the
7 emotion discriminant functions. Each function will return a value. This value will tell us how well that vector fits into that particular
category. The one with the best fit will return the highest value of the 7 categories.

Errors can be calculated based on the number of incorrect classifications.

Tomorrow I hope to move to the next step and start coding the first case discriminant functions using Matlab.