Enhances: cleaner, skimr, ggplot2, tidyselect
Maintainer: Matthijs S. Berends <m.berends@certe.nl>

using evidence-based methods, as described in <doi:10.18637/jss.v104.i03>.
Packaged: 2022-09-27 12:16:58 UTC; msberends

Description: Library to sample and clean text data, build N-gram model, Backoff algorithm etc.
Depends: tm,qdap,RWeka,dplyr, R (>= 2.10)

Title: Feedforward Artificial Neural Network optimized by Genetic
Author: Francis Roy-Desrosiers, based on CUDAANN r6 project developed
Description: This package provides a feedforward Artificial Neural
Network (ANN) optimized by Genetic Algorithm (GA).
Modifications in the Genetic algorithm from CUDAANN r6 have
been done to improve the speed, the convergence and the memory
Depends: R (>= 2.11.0), Rcpp (>= 0.9.0), RcppClassic

Author: Francis Roy-Desrosiers, based on CUDAANN r6 project developed by Emre Caglar.
Description: This package provides a feedforward Artificial Neural Network (ANN) optimized by Genetic Algorithm (GA). Modifications in the Genetic algorithm from CUDAANN r6 have been made to improve the speed, the convergence and the memory management.
Packaged: 2011-11-22 22:48:21 UTC; pavillion

been made to improve the speed, the convergence and the memory

Title: Artificial Neural Networks for Anomaly Detection
Description: Training of general classification and regression neural networks using
gradient descent. Special features include a function for training autoencoders
as well as an implementation of replicator neural networks, for details see
Hawkins et al. (2012) <doi:10.1007/3-540-46145-0_17>. Multiple activation and
cost functions (including Huber and pseudo-Huber) are included, as well as L1
and L2 regularization, momentum, early stopping and the possibility to specify
a learning rate schedule. The package contains a vectorized gradient descent
implementation which facilitates faster training through batch learning.
Packaged: 2017-10-12 07:32:22 UTC; Bart Lammers

gradient descent. Special features include a function for training replicator
neural networks and a function for training autoencoders. Multiple activation and
cost functions (including Huber and pseudo-Huber) are supported, as well as L1