In this problem you will make a prediction for a binary classification problem using kernel- SVM method for the dataset shown in Figure 1. The data set contains eight data points (xi,r2,...,rs) Recall that for any data point r, its predicted label using kenel SVM is given by the equation data # r (label a, rik(zi, z) 5.51 3.35 2 20.8224.03 4.57 19.313.38 10.23 1.05 7.34 08 -2.34 r = sign -1 ESV 0.77 -1 where, the sunmation is taken over all the support vectors (SV). The vector = (ai,O2, ,dg), (whose length is eight, each entry, Lagranges's multiplier, corresponds to one data point) is given a = (1.2.0.0.0.0.0.1.4, 0.3). The kernel used for this problen is given by k(EpFj)-eozh- The 8 8 kernel matrix is shown in Figure 2, Now answer the following questions 14.27 -2.03 11.59 6. -1 (a) Which data points are support vectors in this example? Why? (b) Suppose you want the predict the class label of data #8 using equation 1, What would be the predicted label? Show your work. (c) Using equation1 how will you predict the class label of a new data point (0.1,0.2), i.e., first attribute is 0.1 and the second attribute is 0.2? Show -1 your work. In this problem you will make a prediction for a binary classification problem using kernel- SVM method for the dataset shown in Figure 1. The data set contains eight data points (xi,r2,...,rs) Recall that for any data point r, its predicted label using kenel SVM is given by the equation data # r (label a, rik(zi, z) 5.51 3.35 2 20.8224.03 4.57 19.313.38 10.23 1.05 7.34 08 -2.34 r = sign -1 ESV 0.77 -1 where, the sunmation is taken over all the support vectors (SV). The vector = (ai,O2, ,dg), (whose length is eight, each entry, Lagranges's multiplier, corresponds to one data point) is given a = (1.2.0.0.0.0.0.1.4, 0.3). The kernel used for this problen is given by k(EpFj)-eozh- The 8 8 kernel matrix is shown in Figure 2, Now answer the following questions 14.27 -2.03 11.59 6. -1 (a) Which data points are support vectors in this example? Why? (b) Suppose you want the predict the class label of data #8 using equation 1, What would be the predicted label? Show your work. (c) Using equation1 how will you predict the class label of a new data point (0.1,0.2), i.e., first attribute is 0.1 and the second attribute is 0.2? Show -1 your work