error.TAO {AMORE} | R Documentation |
The error functions calculate the goodness of fit of a neural network according to certain criterium:
error.MSE(arguments) error.LMLS(arguments) error.TAO(arguments) deltaE.MSE(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
Pernia Espinoza, A.V. TAO-robust backpropagation learning algorithm. Neural Networks. In press.
Simon Haykin. Neural Networks. A comprehensive foundation. 2nd Edition.