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:	1.0.4
Depends:	R (≥ 3.0.2), KernelKnn
Imports:	Rcpp (≥ 0.12.17)
LinkingTo:	Rcpp, RcppArmadillo (≥ 0.8)
Suggests:	testthat, covr, knitr, rmarkdown
Published:	2022-01-28
Author:	Lampros Mouselimis [aut, cre], Alberto Gosso [aut], Edwin de Jonge [ctb] (Github Contributor)
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
Citation:	elmNNRcpp citation info
Materials:	README NEWS
CRAN checks:	elmNNRcpp results

Documentation:

Reference manual:	elmNNRcpp.pdf
Vignettes:	Extreme Learning Machine

Downloads:

Package source:	elmNNRcpp_1.0.4.tar.gz
Windows binaries:	r-devel: elmNNRcpp_1.0.4.zip, r-release: elmNNRcpp_1.0.4.zip, r-oldrel: elmNNRcpp_1.0.4.zip
macOS binaries:	r-release (arm64): elmNNRcpp_1.0.4.tgz, r-oldrel (arm64): elmNNRcpp_1.0.4.tgz, r-release (x86_64): elmNNRcpp_1.0.4.tgz, r-oldrel (x86_64): elmNNRcpp_1.0.4.tgz
Old sources:	elmNNRcpp archive

Reverse dependencies:

Reverse imports:

TSPred

Linking:

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