Consider the following set of data points: input desired label 1 1 1 1 0 1...

       

Transcribed Image Text:

Consider the following set of data points: input desired label 1 1 1 1 0 1 0 1 0 -1 -1 0 -1 0 0 -1 1 0 As the above table shows, the data points are categorized (labeled) in two groups specified by the labels "1" and "0". a) Use the perceptron learning rule to train a single neuron perceptron on the data points given above. Show all the steps in the iteration. Assume the learning rate is 1 (ie., n = 1) and use the hard-limiter activation function (If v 20 output 1; otherwise output 0), i.e.: A(v) = {0 [1 if v0 if v <0 b) Draw the schematics of the learned perceptron labeling the learned weights. c) Provide the equation of the trained model. Hint: the question of the classifier line. d) Plot the given data points with two different markers for each group. e) Plot the classifier line to the plot in (d). f) Using the trained perceptron, classify the test data points given in the table below. Show your work for each data point and provide the predicted labels (0 or 1) in a table like below. input X1 X2 desired predict label the label 2 0 1 2 1 0 0 0 1 -2 0 0 g) Provide the confusion matrix for part (f) and calculate the accuracy. Consider the following set of data points: input desired label 1 1 1 1 0 1 0 1 0 -1 -1 0 -1 0 0 -1 1 0 As the above table shows, the data points are categorized (labeled) in two groups specified by the labels "1" and "0". a) Use the perceptron learning rule to train a single neuron perceptron on the data points given above. Show all the steps in the iteration. Assume the learning rate is 1 (ie., n = 1) and use the hard-limiter activation function (If v 20 output 1; otherwise output 0), i.e.: A(v) = {0 [1 if v0 if v <0 b) Draw the schematics of the learned perceptron labeling the learned weights. c) Provide the equation of the trained model. Hint: the question of the classifier line. d) Plot the given data points with two different markers for each group. e) Plot the classifier line to the plot in (d). f) Using the trained perceptron, classify the test data points given in the table below. Show your work for each data point and provide the predicted labels (0 or 1) in a table like below. input X1 X2 desired predict label the label 2 0 1 2 1 0 0 0 1 -2 0 0 g) Provide the confusion matrix for part (f) and calculate the accuracy.