train.compare {AMORE} | R Documentation |
This function trains a neural network according to different error criteria so as to compare the different behaviours.
train.compare(net.start, P, T, ideal=NA, max.epoch, show.step, Stao=1000, criteria=c("MSE","LMLS","TAO"))
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. |
This function returns a list containing the trained Neural Network objects according to the specified criteria.
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
Pernia Espinoza, A.V. TAO-robust backpropagation learning algorithm. Neural Networks. In press.
Simon Haykin. Neural Networks. A comprehensive foundation. 2nd Edition.