neuralnet: Training of Neural Networks

By Frauke Günther. The R Journal, 2010

738 citations recorded by the source corpus.

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Automated Parameter Optimization of Classification Techniques for Defect Prediction Models (2016, positive): “Since we find that parameter settings can improve the performance of the classifiers that are produced (see RQ1), we set out to revisit the findings of prior studies when Caret-optimized settings have been applied.”
Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS (2012, methodology): “This interface can be accessed by the low-level interface functions of RSNNS . neuralnet The package neuralnet (Fritsch et al. 2010) implements standard backpropagation and two types of resilient backpropagation (Riedmiller and Braun 1993; Riedmiller 1994).”
Efficient Water Quality Prediction Using Supervised Machine Learning (2019, methodology): “We used default values for the algorithms, except MLP, which uses a (3, 7) conﬁguration.”
The Impact of Automated Parameter Optimization on Defect Prediction Models (2018, methodology): “varies among settings of 1 for the neuralnet R package [32], 2 for Weka [43] and the nnet R package [117], and 10 for MATLAB [88].”
Variance Stabilizing Transformations for Electricity Spot Price Forecasting (2018, methodology): “For instance, it is used as a link function in generalized linear models (GLM) and is a very popular choice for the activation function in neural nets [31].”