In machine learning, the perceptron is a[1]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.[2]
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.
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Notes
edit- ^ Rojas, Raúl. Neural Networks - A Systematic Introduction. ISBN 978-3-540-60505-8.
- ^ for future