Question: Consider the following loss function for training pair (X,y): L = max{0, a - y(W X)}The test instances are predicted as y = sign{W X}.

Consider the following loss function for training pair (X,y): L = max{0, a - y(W X)}The test instances are predicted as y = sign{W X}. A value of a = 0 corresponds to the perceptron criterion and a value of a = 1 corresponds to the SVM. Show that any value of a > 0 leads to the SVM with an unchanged optimal solution when no regularization is used. What happens when regularization is used?

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