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In machine learning, the perceptron is a n algorithm for supervised learning of binary classifiers(functions that can decide whether an input, represented by a vector of n

umbers, belongs or not to some specific class).[1] 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. The algorithm allows for online learning, in that it processes elements in the training set one at a time.

The perceptron algorithm dates back to the late 1950s; its first implementation, in custom hardware, was one of the first artificial neural networks to be produced.