Q2. (10 points) Consider the following setting. You are provided with n training examples:

(T₁, 9₁, h₁), (2, 92, h₂),, (In, Yn, hn), where z, is the input example, y, is the class label

(+1 or -1), and h₁> 0 is the importance weight of the example. The teacher gave you some

additional information by specifying the importance of each training example. How will you

modify the perceptron algorithm to be able to leverage this extra information? Please justify

your answer.