@misc{10481/39548,
year = {2012},
url = {http://hdl.handle.net/10481/39548},
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.},
organization = {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},
keywords = {SNNS (Stuttgart Neural Network Simulator)},
keywords = {R},
keywords = {RSNNS},
title = {Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS},
doi = {10.18637/jss.v046.i07},
author = {Bergmeir, Christoph Norbert and Benítez Sánchez, José Manuel},
}