train.compare {AMORE}R Documentation

Trains the same neural network according to different error criteria.

Description

This function trains a neural network according to different error criteria so as to compare the different behaviours.

Usage

train.compare(net.start, P, T, ideal=NA, max.epoch, show.step, Stao=1000, criteria=c("MSE","LMLS","TAO")) 

Arguments

net.start Neural Network to train.
P Training set input values.
T Training set output values
ideal Clean training set output values. Useful for training robust networks with noisy data.
max.epoch Number of epochs to train.
show.step A report is provided every show.step epochs.
Stao Initial value of the S parameter used by the TAO algorithm.
criteria A vector specifying which criteria should be used.

Value

This function returns a list containing the trained Neural Network objects according to the specified criteria.

Author(s)

Manuel Castejón Limas. manuel.castejon@unileon.es
Joaquin Ordieres Meré. joaquin.ordieres@dim.unirioja.es
Ana González Marcos. ana.gonzalez@unileon.es
Alpha V. Pernía Espinoza. alpha.pernia@alum.unirioja.es
Eliseo P. Vergara Gonzalez. eliseo.vergara@dim.unirioja.es
Francisco Javier Martinez de Pisón. francisco.martinez@dim.unirioja.es
Fernando Alba Elías. fernando.alba@unavarra.es

References

Pernia Espinoza, A.V. TAO-robust backpropagation learning algorithm. Neural Networks. In press.

Simon Haykin. Neural Networks. A comprehensive foundation. 2nd Edition.

See Also

train


[Package AMORE version 0.1.1 Index]