Mastering Machine Learning with scikit-learn - Second Edition

The perceptron learning algorithm

The perceptron learning algorithm begins by setting the weights to zero, or to small random values. It then predicts the class for a training instance. The perceptron is an error-driven learning algorithm; if the prediction is correct, the algorithm continues to the next instance. If the prediction is incorrect, the algorithm updates the weights. More formally, the update rule is given by the following:

For each training instance, the value of the parameter for each feature is incremented by α(d_j - y_j(t)) x_j,i, where d_j is the true class for instance j, y_j(t) is the predicted class for instance j, x_j,i is ...