Two-Category Classifier

Obtain optimal decision boundary.

Discriminant Functions is one of statistical technique used in Pattern Recognition for separating classses.
This is parametric methods, means, it requires that mean and covariance of class is known. In other words, Probability Density of given class should be known to apply this method.

Here, two classes are chosen to obtain optimal decision boundary betwen two classes.

The classes are 2-dimenSional (Bivariate) and 1-dimensional(Univariate).

It is called Two-Category Classifier. The classifier itself is simplified in three cases:

CASE 1:- In this case feature vectors are statistically independent and covariance matrix is diagonal. samples fall in equal-size spherical clusters.

CASE 2:- In this case feature vectors are statistically dependent but, Covariance matrices are same for both classes.samples fall in equal-size lipsoidal clusters.

CASE 3:- Optimal decision boundary is quadric.

To use this GUI, first unzip the folder. Change current directory to this folder from MATLAB. Then just type discriminant at MATLAB prompt in Command Window and hit ENTER to open the GUI.