Show that, regardless of the dimensionality of the feature vectors, a data set that has just two data points, one from each class, is sufficient to determine the location of the maximum-margin hyperplane. Hint #1: Consider a data set of two data points, X1 E C (y1 = +1) and X2 C2 (y2 = -1) and set up the minimization problem (for computing the hyperplane) with appropriate constraints on w'x+b and w'xg+b and solve it. Hint #2: This can be formed as a constrained optimization problem. arg min ||w| Subject to: (some constraint) What is w? b? Hint: What are the constraints? How did we solve the constrained optimization problem in Fisher's linear discriminate (see Linear Models Lecture Notes or constrained optimization from Calculus)? WERP Show that, regardless of the dimensionality of the feature vectors, a data set that has just two data points, one from each class, is sufficient to determine the location of the maximum-margin hyperplane. Hint #1: Consider a data set of two data points, X1 E C (y1 = +1) and X2 C2 (y2 = -1) and set up the minimization problem (for computing the hyperplane) with appropriate constraints on w'x+b and w'xg+b and solve it. Hint #2: This can be formed as a constrained optimization problem. arg min ||w| Subject to: (some constraint) What is w? b? Hint: What are the constraints? How did we solve the constrained optimization problem in Fisher's linear discriminate (see Linear Models Lecture Notes or constrained optimization from Calculus)? WERP