error.TAO {AMORE}R Documentation

Neural network training error criteria.

Description

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

  • MSE: Mean Squared Error. Least Mean Squares minimization.
  • LMLS: Least Mean Log Squares minimization.
  • TAO: TAO error minimization. The deltaE functions calculate the corresponding influence functions.

    Usage

    error.MSE(arguments)
    error.LMLS(arguments)
    error.TAO(arguments)
    deltaE.MSE(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 neural network.
  • The second element is the target value.
  • A third element is needed for the TAO method containing the value of the S parameter.
  • Value

    This functions return the error and influence function 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, train.compare


    [Package AMORE version 0.1.1 Index]