neuralnet
R package neuralnet: Training of neural networks. Training of neural networks using backpropagation, resilient backpropagation with (Riedmiller, 1994) or without weight backtracking (Riedmiller and Braun, 1993) or the modified globally convergent version by Anastasiadis et al. (2005). The package allows flexible settings through custom-choice of error and activation function. Furthermore, the calculation of generalized weights (Intrator O & Intrator N, 1993) is implemented
Keywords for this software
References in zbMATH (referenced in 10 articles )
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Sorted by year (- Pizarroso, J., Portela, J., Muñoz, A: NeuralSens: Sensitivity Analysis of Neural Networks (2022) not zbMATH
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- Kim, Tae Yoon; Park, Inho: A statistical model of neural network learning via the Cramer-Rao lower bound (2021)
- Barinder Thind, Sidi Wu, Richard Groenewald, Jiguo Cao: FuncNN: An R Package to Fit Deep Neural Networks Using Generalized Input Spaces (2020) arXiv
- Chu, Jianghao; Lee, Tae-Hwy; Ullah, Aman: Component-wise AdaBoost algorithms for high-dimensional binary classification and class probability prediction (2020)
- Mihelač, Lorena; Povh, Janez: AI based algorithms for the detection of (ir)regularity in musical structure (2020)
- Kozlowski, Steven E.; Sim, Thaddeus: Predicting recessions using trends in the yield spread (2019)
- Bose, A.; Patel, G. N.: “NeuralDEA” -- a framework using neural network to re-evaluate DEA benchmarks (2015)
- Cano, Emilio L.; Moguerza, Javier M.; Redchuk, Andrés: Six Sigma with R. Statistical engineering for process improvement. (2012)
- Wehrens, Ron: Chemometrics with R. Multivariate data analysis in the natural sciences and life sciences (2011)