deltaE: Neural network training error criteria.

Description Usage Arguments Value Author(s) References See Also

Description

The error functions calculate the goodness of fit of a neural network according to certain criterium:

The deltaE functions calculate the influence functions of their error criteria.

Usage

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error.LMS(arguments)
error.LMLS(arguments)
error.TAO(arguments)
deltaE.LMS(arguments)
deltaE.LMLS(arguments)
deltaE.TAO(arguments)

Arguments

arguments

List of arguments to pass to the functions.

  • The first element is the prediction of the neuron.

  • The second element is the corresponding component of the target vector.

  • The third element is the whole net. This allows the TAO criterium to know the value of the S parameter and eventually ( next minor update) will allow the user to apply regularization criteria.

Value

This functions return the error and influence function criteria.

Author(s)

Manuel Castejón Limas. manuel.castejon@gmail.com
Joaquin Ordieres Meré. j.ordieres@upm.es
Ana González Marcos. ana.gonzalez@unirioja.es
Alpha V. Pernía Espinoza. alpha.pernia@unirioja.es
Francisco Javier Martinez de Pisón. fjmartin@unirioja.es
Fernando Alba Elías. fernando.alba@unavarra.es

References

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.

See Also

train


AMORE documentation built on Feb. 12, 2020, 9:07 a.m.

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