Lagrange Multiplier 64 X1 X2 0.3 0.4 0.5 0.6 +1 0.9 | 0.4 | +1 0.7 0.8 +1 -1 65 0.2 -1 0. 0.4 0.3 -1 0.9 0.8 +1 0.1 -1 Question 1: Consider the two-dimensional data set shown in the following table,...

Extracted text: Lagrange Multiplier 64 X1 X2 0.3 0.4 0.5 0.6 +1 0.9 | 0.4 | +1 0.7 0.8 +1 -1 65 0.2 -1 0. 0.4 0.3 -1 0.9 0.8 +1 0.1 -1 Question 1: Consider the two-dimensional data set shown in the following table, which contains eight training instances. Using quadratic programming, we can solve the optimization problem to obtain the Lagrange multiplier 2, for each training instance. The Lagrange multipliers are depicted in the last column of the table. These instances correspond to the support vectors for this data set. Let w = boundary. a) if-1 denotes square and +1 denotes circle, sketch the scatterplot in a 2-dimensional plane. b) calculate w and wz. c) calculate the bias term b and b2) for each support vector. d) obtain average bias term value b e) sketch the decision boundary corresponding to above parameters. (Wt, W2) and b denote the parameters of the decision