Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS

By Christoph Bergmeir, José Manuel Benítez. 2012

313 citations recorded by the source corpus.

Neural networks are important standard machine learning procedures for classification and regression. We describe the R package RSNNS that provides a convenient interface to the popular Stuttgart Neural Network Simulator SNNS. The main features are (a) encapsulation of the relevant SNNS parts in a C++ class, for sequential and parallel usage of different networks, (b) accessibility of all of the SNNS algorithmic functionality from R using a low-level interface, and (c) a high-level interface for convenient, R-style usage of many standard neural network procedures. The package also includes functions for visualization and analysis of the models and the training procedures, as well as functions for data input/output from/to the original SNNS file formats.

Subfields: Artificial Intelligence, Control and Systems Engineering, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Kjell Johnson
2. Johannes W. Kruisselbrink
3. Ron Wehrens
4. Max Kuhn
5. Miron Bartosz Kursa
6. David A. Noever
7. Przemyslaw Biecek
8. Abdulhakim M. Abdi

In Control and Systems Engineering

1. Ron Wehrens
2. Johannes W. Kruisselbrink
3. Max Kuhn
4. Kjell Johnson

In Statistical and Nonlinear Physics

1. Ron Wehrens
2. Johannes W. Kruisselbrink
3. Max Kuhn
4. Kjell Johnson

Citations in the corpus

• Applied Predictive Modeling (2013, background): “Bergmeir and Benitez (2012) outline the various neural network packages in R and contain a tutorial on RSNNS.”
• An Introductory Review of Deep Learning for Prediction Models With Big Data (2020, unknown): “…BigDL (python) (Dai et al., 2018), Caffe (python) (Jia et al., 2014) Multilayer Perceptron (MLP) SparkR (R) (Venkataraman et al., 2016), RSNNS (R) (Bergmeir and Benítez, 2012), keras (R and python) ( Chollet, 2015), sklearn (python) (Pedregosa et al., 2011), tensorflow (R and python) (Abadi et…”
• Flexible Self-Organizing Maps in kohonen 3.0 (2018, methodology): “The RSNNS package (Bergmeir and Benítez 2012) is an R wrapper to the SNNS toolbox written in C++ , including SOMs as one of many types of neural networks.”