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Please use this identifier to cite or link to this item: http://hdl.handle.net/10481/39548

Title: Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS
Authors: Bergmeir, Christoph Norbert
Benítez Sánchez, José Manuel
Issue Date: 2012
Abstract: 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.
Sponsorship: This work was supported in part by the Spanish Ministry of Science and Innovation (MICINN) under Project TIN-2009-14575. C. Bergmeir holds a scholarship from the Spanish Ministry of Education (MEC) of the \Programa de Formación del Profesorado Universitario (FPU)".
Publisher: American Statistical Association
Keywords: Neural networks
SNNS (Stuttgart Neural Network Simulator)
R
RSNNS
URI: http://hdl.handle.net/10481/39548
ISSN: 1548-7660
Rights : Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License
Citation: Bergmeir, C.N.; Benítez Sánchez, J.M. Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS. Journal of Statistical Software, 46(7): online (2012). [http://hdl.handle.net/10481/39548]
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