Question: Consider an SVM with decision boundary w x + b = 0 for a threedimensional feature space. The learned weight vector is w = [

Consider an SVM with decision boundary w x + b =0 for a threedimensional
feature space. The learned weight vector is w =[1,2,3] and b =4. For a data point (x1=
[0,0,0], y1=1) if it was in the training set, what is the smallest slack variable that would satifsy the
margin constraint?

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