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What is Back-Propagation

Chapter 52
Encyclopedia of Data Science and Machine Learning
A supervised learning method used to determine the weights of an ANN, where the difference between the desired and the model’s output is minimized.
Published in Chapter:
Artificial Neural Networks and Data Science
Trevor Bihl (Air Force Research Laboratory, USA), William A. Young II (Ohio University, USA), Adam Moyer (Ohio University, USA), and Steven Frimel (Air Force Research Laboratory, USA)
Copyright: © 2023 |Pages: 23
DOI: 10.4018/978-1-7998-9220-5.ch052
Abstract
Artificial neural networks (ANNs) consist of a family of techniques that are commonly employed to recognize and interpret patterns in big data that are used in prediction, clustering, classification, and identification of other previously unknown data patterns. This article describes foundational concepts that relate to ANNs, including an understanding of how ANNs are linked to biological concepts and the underlying ANN families. The article includes an explanation of common ANN methods, architecture/hyperparameter determination for initializing ANNs, and current research directions. The article concludes with a discussion on the need for algorithmic transparency and repeatability of research.
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Chapter 148
Learning in Feed-Forward Artificial Neural Networks I
Algorithm for feed-forward multilayer networks that can be used to efficiently compute the gradient vector in all the first-order methods.
Published in Chapter: Learning in Feed-Forward Artificial Neural Networks I; From: Encyclopedia of Artificial Intelligence
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Chapter 19
Artificial Neural Networks for Business Analytics
A supervised learning method used to determine the weights of an ANN, where the difference between the desired and the model’s output is minimized.
Published in Chapter: Artificial Neural Networks for Business Analytics; From: Encyclopedia of Business Analytics and Optimization
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Chapter 16
Artificial Neural Networks
The training algorithm for the feed-forward, multi-layer perceptron networks which works by propagating errors back through a network and adjusting weights in the direction opposite to the largest local gradient.
Published in Chapter: Artificial Neural Networks; From: Handbook of Research on Geoinformatics
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Chapter 576
Artificial Neural Networks and Their Applications in Business
A supervised learning method used to determine the weights of an ANN, where the difference between the desired and the model’s output is minimized.
Published in Chapter: Artificial Neural Networks and Their Applications in Business; From: Encyclopedia of Information Science and Technology, Fourth Edition
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Chapter 49
AI-Based Emotion Recognition
Backpropagation is an algorithm to fine-tune weights and biases to improve the accuracy and reduce error for the artificial neural network outputs.
Published in Chapter: AI-Based Emotion Recognition; From: Encyclopedia of Data Science and Machine Learning
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