We have a training set with 4 data points, as follows : X = (0, 0) => Y = 1 : X = (0, 1) => Y = -1 : X = (1, 0) => Y = -1 : X = (1, 1) => Y = 1. Notice that the data above is not linearly separable, hence the perceptron algorithm will not be able to learn a classifier that gives the correct prediction for all four above data points. Add a 3rd dimension to each of the extra input dimension so that the data becomes linearly separable:
Third value is equal to target value for each data point
Third value is opposite of target value for each data point
Both of the above
None of the above
