Following training samples are given: X1 X2 Class 1 1 +1 -1 -1 -1 0 0.5...

    

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Following training samples are given: X1 X2 Class 1 1 +1 -1 -1 -1 0 0.5 -1 0.1 0.5 -1 0.2 0.2 +1 0.9 0.5 +1 Table 1: Sample data Assuming weight vector of initial decision boundary wx = 0 as w=[1, 1], solve the following: 1. In how many steps perception learning algorithm will converge. (15 points) 2. What will be the final decision boundary? Show step-wise-step update of weight vector using computation as well as hand-drawn plot. (15 points) 3. Prove that Perceptron Learning Algorithm converges in a finite number of steps. (10 points) Following training samples are given: X1 X2 Class 1 1 +1 -1 -1 -1 0 0.5 -1 0.1 0.5 -1 0.2 0.2 +1 0.9 0.5 +1 Table 1: Sample data Assuming weight vector of initial decision boundary wx = 0 as w=[1, 1], solve the following: 1. In how many steps perception learning algorithm will converge. (15 points) 2. What will be the final decision boundary? Show step-wise-step update of weight vector using computation as well as hand-drawn plot. (15 points) 3. Prove that Perceptron Learning Algorithm converges in a finite number of steps. (10 points)