5.4 (**) Consider a binary classification problem in which the target values are te {0,1}, with a network output y(x, w) that represents p(t = 11x), and suppose that there is a probability e that the class label on a training data point has been incorrectly set. Assuming independent and identically distributed data, write down the error function corresponding to the negative log likelihood. Verify that the error function (5.21) is obtained when e = 0. Note that this error function makes the model robust to incorrectly labelled data, in contrast to the usual error function. 5.4 (**) Consider a binary classification problem in which the target values are te {0,1}, with a network output y(x, w) that represents p(t = 11x), and suppose that there is a probability e that the class label on a training data point has been incorrectly set. Assuming independent and identically distributed data, write down the error function corresponding to the negative log likelihood. Verify that the error function (5.21) is obtained when e = 0. Note that this error function makes the model robust to incorrectly labelled data, in contrast to the usual error function