7 Class 1 Class 2 6 5 + 8 et 3 + N + 1 0 2 3 4 7 X A decision boundary which leads to the largest margin is learnt by the Support Vector Machines. Consider that SVM trains on Dataset I, given in the above figure consisting of 6 data points in total as shown. There are two class labels, namely - Class Label 1, which is denoted by a blue plus and Class Label 2, which is denoted by a red diamond. (a) [1 Points] Given the Dataset I above and the support vectors (4, 5) and (2.3), what is the slope of the decision boundary? (b) [3 Points] Based on the slope calculated above, find the normal vector (w) and bias (wo). Based on the above findings, what is the equation to represent the decision boundary? Note - decision boundary should follow wo + w1x1 + w2x2 = 0 and the normal vector is of the form (W1,W2). Use the point slope form. 1 (c) [1 Points] Based on the decision boundary, provide any other support vectors. 7 Class 1 Class 2 6 5 + 8 et 3 + N + 1 0 2 3 4 7 X A decision boundary which leads to the largest margin is learnt by the Support Vector Machines. Consider that SVM trains on Dataset I, given in the above figure consisting of 6 data points in total as shown. There are two class labels, namely - Class Label 1, which is denoted by a blue plus and Class Label 2, which is denoted by a red diamond. (a) [1 Points] Given the Dataset I above and the support vectors (4, 5) and (2.3), what is the slope of the decision boundary? (b) [3 Points] Based on the slope calculated above, find the normal vector (w) and bias (wo). Based on the above findings, what is the equation to represent the decision boundary? Note - decision boundary should follow wo + w1x1 + w2x2 = 0 and the normal vector is of the form (W1,W2). Use the point slope form. 1 (c) [1 Points] Based on the decision boundary, provide any other support vectors