Perceptron

In computational geometry, the perceptron is an algorithm for supervised classification of an input into one of two possible outputs. It is a type of linear classifier, i.e. a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector describing a given input. The learning algorithm for perceptrons is an online algorithm, in that it processes elements in the training set one at a time.

The perceptron algorithm was invented in 1957 at the Cornell Aeronautical Laboratory by Frank Rosenblatt.

In the context of artificial neural networks, the perceptron algorithm is also termed the single-layer perceptron, to distinguish it from the case of a multilayer perceptron, which is a more complicated neural network. As a linear classifier, the (single-layer) perceptron is the simplest kind of feedforward neural network.