Perceptron Algorithm

The perceptron algorithm learns the weights of a linear binary classifier by processing training examples one at a time, adjusting the weight vector whenever the current classifier misclassifies an example, moving the decision boundary in the direction that would have classified that example correctly, and repeating over the training set until no more mistakes are made or a maximum number of passes is reached. Frank Rosenblatt introduced it in 1958 as a model of a simplified biological neuron and the learning rule for one of the earliest artificial neural network models. It is guaranteed to converge to a separating hyperplane when the training data is linearly separable, the perceptron convergence theorem.