We are given n = 7 observations in p = 2 dimensions. For each observation, there is an associated class label. . X = (3,4), Y = 1; . X = (2,2), Y = 1; . X = (4,4), Y = 1; . X = (1,4), Y =1; . X = (3, 1), Y = -1; . X = (4,3), Y = -1; . X = (4, 1), Y = -1; We fit a linear SVM on this data set and have found that the 2nd, 3rd, and 6th obs are the support vectors. The corresponding lambda values for the the 2nd and 3rd observations are . A2 = 1, A3 = 3. Write the corresponding decision rule as: if by X1 + b2X2 + 1 > 0, then predict Y = 1. Then bj = [bi], b2 = [be]. (b1 and by take integer values.)