In a three-class, two-dimensional problem the feature vectors in each class are normally distributed with covariance...

Transcribed Image Text:

In a three-class, two-dimensional problem the feature vectors in each class are normally distributed with covariance matrix 1.2 0.4 0.4 1.8 The mean vectors for each class are [0.1,0.1], [2.1, 1.9], [-1.5, 2.01¹. Assuming that the classes are equiprobable, (a) classify the feature vec- tor [1.6, 1.5] according to the Bayes minimum error probability classifier; (b) draw the curves of equal Mahalanobis distance from [2.1, 1.91]. In a three-class, two-dimensional problem the feature vectors in each class are normally distributed with covariance matrix 1.2 0.4 0.4 1.8 The mean vectors for each class are [0.1,0.1], [2.1, 1.9], [-1.5, 2.01¹. Assuming that the classes are equiprobable, (a) classify the feature vec- tor [1.6, 1.5] according to the Bayes minimum error probability classifier; (b) draw the curves of equal Mahalanobis distance from [2.1, 1.91].