We consider the following points: A: (1, 5)- B: (5, 8) + C: (3, 4) + D: (1, 3) + E: (6, -1) - F: (10, -2) + G: (8, -4)- We want to run the perceptron algorithm on them Specifically, we initialize the weight vector at (0, 0) and we consider the stream of points A - > B - > D - > E - > C - > F - > G We say that the first 4 points are used for training (i.e. to learn w) and the remaining 3 points are used to evaluate performance. We use the following learning rates: 2, 1, 0.5, 0.25. Compute the value of w after the training and the misclassification error. a) After the 3rd step of training w = (3.5, -0.5) and point E is misclassified. b) w_final (after 4 steps) = (2, 0.25) and point G is misclassified. c) w_final (after 4 steps) = (-2, -1.25) and the classification error is 2/3. d) w_final (after 4 steps) = (2, 0.25) and the classification error is 1/3. We consider the following points: A: (1, 5)- B: (5, 8) + C: (3, 4) + D: (1, 3) + E: (6, -1) - F: (10, -2) + G: (8, -4)- We want to run the perceptron algorithm on them Specifically, we initialize the weight vector at (0, 0) and we consider the stream of points A - > B - > D - > E - > C - > F - > G We say that the first 4 points are used for training (i.e. to learn w) and the remaining 3 points are used to evaluate performance. We use the following learning rates: 2, 1, 0.5, 0.25. Compute the value of w after the training and the misclassification error. a) After the 3rd step of training w = (3.5, -0.5) and point E is misclassified. b) w_final (after 4 steps) = (2, 0.25) and point G is misclassified. c) w_final (after 4 steps) = (-2, -1.25) and the classification error is 2/3. d) w_final (after 4 steps) = (2, 0.25) and the classification error is 1/3