Question: Consider the following loss function for training pair ( X , y ) : L = max { 0 , a y ( W X

Consider the following loss function for training pair (X,y):
L=max{0,ay(WX)}
The test instances are predicted as y^=sign{WXT}. 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?
2.
Based on Exercise 1, formulate a generalized objective for the Weston-Watkins SVM

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