error.TAO {AMORE} | R Documentation |
The error functions calculate the goodness of fit of a neural network according to certain criterium:
error.LMS(arguments) error.LMLS(arguments) error.TAO(arguments) deltaE.LMS(arguments) deltaE.LMLS(arguments) deltaE.TAO(arguments)
arguments |
List of arguments to pass to the functions.
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This functions return the error and influence function 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
Pernía Espinoza, A.V., Ordieres Meré, J.B., Martínez de Pisón, F.J., González Marcos, A. TAO-robust backpropagation learning algorithm. Neural Networks. Vol. 18, Issue 2, pp. 191–204, 2005.
Simon Haykin. Neural Networks – a Comprehensive Foundation. Prentice Hall, New Jersey, 2nd edition, 1999. ISBN 0-13-273350-1.