Package: FCNN4R
Type: Package
Title: Fast Compressed Neural Networks for R
Version: 0.6.0
Date: 2015-12-31
Author: Grzegorz Klima <gklima@users.sourceforge.net>
Maintainer: Grzegorz Klima <gklima@users.sourceforge.net>
Description: Provides an interface to kernel routines from the FCNN C++ library.
FCNN is based on a completely new Artificial Neural Network representation that
offers unmatched efficiency, modularity, and extensibility. FCNN4R provides
standard teaching (backpropagation, Rprop, simulated annealing, stochastic
gradient) and pruning algorithms (minimum magnitude, Optimal Brain Surgeon),
but it is first and foremost an efficient computational engine. Users can
easily implement their algorithms by taking advantage of fast gradient computing
routines, as well as network reconstruction functionality (removing weights
and redundant neurons, reordering inputs, merging networks). Networks can be
exported to C functions in order to integrate them into virtually any software
solution.
Depends: R (>= 3.0), stats, graphics, methods, Rcpp
License: GPL (>= 2)
NeedsCompilation: yes
Packaged: 2015-12-31 19:45:03 UTC; chaos
Repository: CRAN
Date/Publication: 2015-12-31 23:31:42

Package: FCNN4R
Type: Package
Title: Fast Compressed Neural Networks for R
Version: 0.6.2
Date: 2016-03-08
Author: Grzegorz Klima <gklima@users.sourceforge.net>
Maintainer: Grzegorz Klima <gklima@users.sourceforge.net>
Description: Provides an interface to kernel routines from the FCNN C++ library.
FCNN is based on a completely new Artificial Neural Network representation that
offers unmatched efficiency, modularity, and extensibility. FCNN4R provides
standard teaching (backpropagation, Rprop, simulated annealing, stochastic
gradient) and pruning algorithms (minimum magnitude, Optimal Brain Surgeon),
but it is first and foremost an efficient computational engine. Users can
easily implement their algorithms by taking advantage of fast gradient computing
routines, as well as network reconstruction functionality (removing weights
and redundant neurons, reordering inputs, merging networks). Networks can be
exported to C functions in order to integrate them into virtually any software
solution.
Depends: R (>= 3.0), stats, graphics, methods, Rcpp
License: GPL (>= 2)
NeedsCompilation: yes
Packaged: 2016-03-08 19:52:41 UTC; chaos
Repository: CRAN
Date/Publication: 2016-03-09 00:57:57