Statistics, Department of
The R Journal
Date of this Version
6-2010
Document Type
Article
Citation
The R Journal (June 2010) 2(1)
Abstract
Artificial neural networks are applied in many situations. neuralnet is built to train multi-layer perceptrons in the context of regression analyses, i.e. to approximate functional relationships between covariates and response variables. Thus, neural networks are used as extensions of generalized linear models. neuralnet is a very flexible package. The back propagation algorithm and three versions of resilient back-propagation are implemented and it provides a custom-choice of activation and error function. An arbitrary number of covariates and response variables as well as of hidden layers can theoretically be included. The paper gives a brief introduction to multi-layer perceptrons and resilient back-propagation and demonstrates the application of neuralnet using the data set infert, which is contained in the R distribution.
Included in
Numerical Analysis and Scientific Computing Commons, Programming Languages and Compilers Commons
Comments
Copyright 2010, The R Foundation. Open access material. License: CC BY 3.0 Unported