🔬Data Science: Perceptron (Definitions)

"the term is often used to refer to a single layer pattern classification network with linear threshold units" (Laurene V Fausett, "Fundamentals of Neural Networks: Architectures, Algorithms, and Applications", 1994)

"adaptive element for multilayer feedforward networks introduced by Rosenblatt" (Teuvo Kohonen, "Self-Organizing Maps" 3rd Ed., 2001)

"An early theoretical model of the neuron developed by Rosenblatt (1958) that was the first to incorporate a learning rule. The term is also used as a generic label for all trained feed-forward networks, which is often referred to collectively as multilayer perceptron networks." (David Scarborough & Mark J Somers, "Neural Networks in Organizational Research: Applying Pattern Recognition to the Analysis of Organizational Behavior", 2006)

"A type of binary classifier that maps its inputs (a vector of real type) to an output value (a scalar real type). The perceptron may be considered as the simplest model of feed-forward neural network, as the inputs directly feeding the output units through weighted connections." )Crescenzio Gallo, "Artificial Neural Networks Tutorial", 2015)

"A perceptron is a type of a neural network organized into layers where each layer receives connections from units in the previous layer and feeds its output to the units of the layer that follow." (Ethem Alpaydın, "Machine learning : the new AI", 2016)

"Perceptron is a learning algorithm which is used to learn the decision boundary for linearly separable data." Vandana M Ladwani, "Support Vector Machines and Applications", 2017)

"A simple neural network model consisting of one unit and inputs with variable weights that can be trained to classify inputs into categories." (Terrence J Sejnowski, "The Deep Learning Revolution", 2018)

"The simplest form of artificial neural network, a basic operational unit which employs supervised learning. It is used to classify data into two classes." (Gaetano B Ronsivalle & Arianna Boldi, "Artificial Intelligence Applied: Six Actual Projects in Big Organizations", 2019)

"A perceptron is a single-layer neural network. It includes input values, weights and bias, net sum, and an activation function." (Prisilla Jayanthi & Muralikrishna Iyyanki, "Deep Learning Techniques for Prediction, Detection, and Segmentation of Brain Tumors", 2020)

"The basic unit of a neural network that encodes inputs from neurons of the previous layer using a vector of weights or parameters associated with the connections between perceptrons." Mário P Véstias, "Deep Learning on Edge: Challenges and Trends", 2020)

"these are machine learning algorithms that undertake supervised learning of functions called binary classifiers which decide whether or not an input, usually identified with a vector of numbers, belongs to a particular class." (Hari Kishan Kondaveeti et al, "Deep Learning Applications in Agriculture: The Role of Deep Learning in Smart Agriculture", 2021)