Introduction to Pattern Recognition: A Matlab Approach

1.4 Minimum Distance Classifiers 7

Solution. Take the following steps:

Step 1. Use the function euclidean_classifier by typing

x=[0.1 0.5 0.1]';

m1=[0 0 0]'; m2=[0.5 0.5 0.5]';

m=[m1 m2];

z=euclidean_classifier(m,x)

The answer is z = 1; that i s, t he point is classi fied t o the ω

1

class.

Step 2. Use the function mahalanobis_classifier by typing

x=[0.1 0.5 0.1]';

m1=[0 0 0]'; m2=[0.5 0.5 0.5]';

m=[m1 m2];

S=[0.8 0.01 0.01;0.01 0.2 0.01; 0.01 0.01 0.2];

z=mahalanobis_classifier(m,S,x);

This time, the answer is z = 2, meaning the point is classified to the second class. For this case, the

optimal Bayesian classifier i s realized by t he Mahal anobis distance clas ...