Q5. (15 points) Suppose we have n positive training examples and n negative training examples. Let C be the center of the positive examples and C be the center of the negative called CLOSE that classifies a test example x by assigning it to the class whose center is Show that the decision boundary of the CLOSE classifier is a linear hyperplane of the Recal that the weight vector can be written as a linear combination of all the training examples, i.e., C+- yitiz, and C-n. n--ITp Consider a simple classifier 12:i. Consider a simple classilier i: w closest. form sign(w-x+b). Compute the values of w and b in terms of C+ and C. examples: w-'altn ai . yi T Compute the dual weights (a's). How many of the training examples are support vectors? Q5. (15 points) Suppose we have n positive training examples and n negative training examples. Let C be the center of the positive examples and C be the center of the negative called CLOSE that classifies a test example x by assigning it to the class whose center is Show that the decision boundary of the CLOSE classifier is a linear hyperplane of the Recal that the weight vector can be written as a linear combination of all the training examples, i.e., C+- yitiz, and C-n. n--ITp Consider a simple classifier 12:i. Consider a simple classilier i: w closest. form sign(w-x+b). Compute the values of w and b in terms of C+ and C. examples: w-'altn ai . yi T Compute the dual weights (a's). How many of the training examples are support vectors