We want to train a perceptron network using the following training set:starting from the initial conditionsi. Sketch the initial decision boundary, and show the weight vector and the three training input vectors, . Indicate the class of each input vector, P1, P2 , P3 and show which ones are correctly classified by the initial decision boundary.ii. Present the input P1 to the network, and perform one iteration of the perceptron learning rule.iii. Sketch the new decision boundary and weight vector, and again indicate which of the three input vectors are correctly classified.iv. Present the input P2 to the network, and perform one more iteration of the perceptron learning rule.v. Sketch the new decision boundary and weight vector, and again indicate which of the three input vectors are correctly classified.vi. If you continued to use the perceptron learning rule, and presented all of the patterns many times, would the network eventually learn to correctly classify the patterns? Explain your answer. (This part does not require any calculations.)
We want to train a perceptron network using the following training set: starting from the initial…
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