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Perceptron - Wikipedia

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In machine learning, the perceptron (or McCulloch–Pitts neuron) is an algorithm for supervised learning of binary classifiers. A binary classifier is a function which can decide whether or not an input, represented by a vector of numbers, belongs 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 perceptron was invented in 1943 by Warren McCulloch and Walter Pitts.[5] The first hardware implementation was Mark I Perceptron machine built in 1957 at the Cornell Aeronautical Laboratory by Frank Rosenblatt,[6] funded by the Information Systems Branch of the United States Office of Naval Research and the Rome Air Development Center. It was first publicly demonstrated on 23 June 1960.[7] The machine was "part of a previously secret four-year NPIC [the US' National Photographic Interpretation Center] effort from 1963 through 1966 to

Perceptron - Wikipedia Jump to content From Wikipedia, the free encyclopedia Algorithm for supervised learning of binary classifiers "Perceptrons" redirects here. For the 1969 book, see Perceptrons (book) . Part of a series on Machine learning and data mining Paradigms Supervised learning Unsupervised learning Semi-supervised learning Self-supervised learning Reinforcement learning Meta-learning Online learning Batch learning Curriculum learning Rule-based learning Neuro-symbolic AI Neuromorphic engineering Quantum machine learning Problems Classification Generative modeling Regression Cluster

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