This is all the information we are given for the problem.

6. A SVM is trained with the following data: (10 Points] X1 x2 class -1 -1 -1 1 1 1 0 2 1 Let , a, and az be the lagrangian multipliers for the three data points. a. Using polynomial kernel of degree 2 what (dual) optimization problem needs to be solved in terms of the lagrangian multipliers in order to determine their values? The polynomial kernel of degree d is given by the equation K(X; ,x;) = (1 + xx;)" where X and xj are input vectors b. Let us say that we have solved the optimization problem and found that ay = az = =and az = 0. Moreover b = 0. Can you tell me which of the data points are support vectors. Explain your answer. C. Assuming Q1 = Q2 = =, Az = 0 and b = 0, how will the SVM classify the point (x1 = -1, x2 = 0)? Explain your answer? d. Assuming ay = az = , Az = 0 and b = 0, how will the SVM classify the point (x1 = 1, X2 = 0)? Explain your answer? 6. A SVM is trained with the following data: (10 Points] X1 x2 class -1 -1 -1 1 1 1 0 2 1 Let , a, and az be the lagrangian multipliers for the three data points. a. Using polynomial kernel of degree 2 what (dual) optimization problem needs to be solved in terms of the lagrangian multipliers in order to determine their values? The polynomial kernel of degree d is given by the equation K(X; ,x;) = (1 + xx;)" where X and xj are input vectors b. Let us say that we have solved the optimization problem and found that ay = az = =and az = 0. Moreover b = 0. Can you tell me which of the data points are support vectors. Explain your answer. C. Assuming Q1 = Q2 = =, Az = 0 and b = 0, how will the SVM classify the point (x1 = -1, x2 = 0)? Explain your answer? d. Assuming ay = az = , Az = 0 and b = 0, how will the SVM classify the point (x1 = 1, X2 = 0)? Explain your