Consider the problem of classifying 10 samples from Table 1 . Assume that the underlying distributions are normal. For each category, the mean vector and the covariance matrix are given by =101k=110xk=101k=110(xk)(xk)t where xk denotes the k-th samples in that category. (a) (10%) Assume that the prior probabilities for each category are as follows: P(1)=0.2,P(2)=0.5,P(3)=0.3 Determine mean vectors and covariance matrices for these three categories using x1 and x2 feature values. (b) (10%) Calculate the percentage of misclassified samples for each category. (c) (20%) Repeat all of the above, but now use three feature values (i.e., x1,x2, and x3) Table 1: Computer exercise relies on the following data. Consider the problem of classifying 10 samples from Table 1 . Assume that the underlying distributions are normal. For each category, the mean vector and the covariance matrix are given by =101k=110xk=101k=110(xk)(xk)t where xk denotes the k-th samples in that category. (a) (10%) Assume that the prior probabilities for each category are as follows: P(1)=0.2,P(2)=0.5,P(3)=0.3 Determine mean vectors and covariance matrices for these three categories using x1 and x2 feature values. (b) (10%) Calculate the percentage of misclassified samples for each category. (c) (20%) Repeat all of the above, but now use three feature values (i.e., x1,x2, and x3) Table 1: Computer exercise relies on the following data