Consider the reference patterns and targets given below. We want to use these data to train...

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Consider the reference patterns and targets given below. We want to use these data to train a linear associator network. P1 P₁ = = [4]." = [26] } 41 P2 {P₁ = [2]-12= [26]} {P₁ = [2] = [-26]} P3 13 i. Use the Hebb rule to find the weights of the network. ii. Find and sketch the decision boundary for the network with the Hebb rule weights. iii. Use the pseudo-inverse rule to find the weights of the network. Be- cause the number, R, of rows of P is less than the number of col- umns, Q, of P, the pseudoinverse can be computed by P+ = P(PPT) iv. Find and sketch the decision boundary for the network with the pseudo-inverse rule weights. v. Compare (discuss) the decision boundaries and weights for each of w the methods (Hebb and pseudo-inverse). Go to Settings to activate V Consider the reference patterns and targets given below. We want to use these data to train a linear associator network. P1 P₁ = = [4]." = [26] } 41 P2 {P₁ = [2]-12= [26]} {P₁ = [2] = [-26]} P3 13 i. Use the Hebb rule to find the weights of the network. ii. Find and sketch the decision boundary for the network with the Hebb rule weights. iii. Use the pseudo-inverse rule to find the weights of the network. Be- cause the number, R, of rows of P is less than the number of col- umns, Q, of P, the pseudoinverse can be computed by P+ = P(PPT) iv. Find and sketch the decision boundary for the network with the pseudo-inverse rule weights. v. Compare (discuss) the decision boundaries and weights for each of w the methods (Hebb and pseudo-inverse). Go to Settings to activate V

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Solutions Step 1 The question pertains to training a linear associator network using two different methods the Hebb rule and the pseudoinverse rule In... View the full answer