elmNNRcpp: The Extreme Learning Machine Algorithm

Training and predict functions for Single Hidden-layer Feedforward Neural Networks (SLFN) using the Extreme Learning Machine (ELM) algorithm. The ELM algorithm differs from the traditional gradient-based algorithms for very short training times (it doesn't need any iterative tuning, this makes learning time very fast) and there is no need to set any other parameters like learning rate, momentum, epochs, etc. This is a reimplementation of the 'elmNN' package using 'RcppArmadillo' after the 'elmNN' package was archived. For more information, see "Extreme learning machine: Theory and applications" by Guang-Bin Huang, Qin-Yu Zhu, Chee-Kheong Siew (2006), Elsevier B.V, <doi:10.1016/j.neucom.2005.12.126>.

Version: 1.0.1
Depends: R (≥ 3.0.2), KernelKnn
Imports: Rcpp (≥ 0.12.17)
LinkingTo: Rcpp, RcppArmadillo (≥ 0.8)
Suggests: testthat, covr, knitr, rmarkdown
Published: 2018-07-21
Author: Lampros Mouselimis [aut, cre], Alberto Gosso [aut]
Maintainer: Lampros Mouselimis <mouselimislampros at gmail.com>
BugReports: https://github.com/mlampros/elmNNRcpp/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/mlampros/elmNNRcpp
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: elmNNRcpp results

Downloads:

Reference manual: elmNNRcpp.pdf
Vignettes: Extreme Learning Machine
Package source: elmNNRcpp_1.0.1.tar.gz
Windows binaries: r-devel: elmNNRcpp_1.0.0.zip, r-release: elmNNRcpp_1.0.0.zip, r-oldrel: elmNNRcpp_1.0.1.zip
OS X binaries: r-release: elmNNRcpp_1.0.1.tgz, r-oldrel: elmNNRcpp_1.0.1.tgz
Old sources: elmNNRcpp archive

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