error.TAO               package:AMORE               R Documentation

_N_e_u_r_a_l _n_e_t_w_o_r_k _t_r_a_i_n_i_n_g _e_r_r_o_r _c_r_i_t_e_r_i_a.

_D_e_s_c_r_i_p_t_i_o_n:

     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.

_U_s_a_g_e:

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

_A_r_g_u_m_e_n_t_s:

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.

_V_a_l_u_e:

     This functions return the error and influence function criteria.

_A_u_t_h_o_r(_s):

     Manuel Castejn Limas.              manuel.castejon@unileon.es
      Joaquin Ordieres Mer.             
     joaquin.ordieres@dim.unirioja.es
      Ana Gonzlez Marcos.                ana.gonzalez@unileon.es 
      Alpha V. Perna Espinoza.           alpha.pernia@alum.unirioja.es
      Eliseo P. Vergara Gonzalez.        
     eliseo.vergara@dim.unirioja.es
      Francisco Javier Martinez de Pisn.
     francisco.martinez@dim.unirioja.es
      Fernando Alba Elas.                fernando.alba@unavarra.es

_R_e_f_e_r_e_n_c_e_s:

     Pernia Espinoza, A.V. TAO-robust backpropagation learning
     algorithm. Neural Networks. In press. 

      Simon Haykin. Neural Networks. A comprehensive foundation. 2nd
     Edition. 


_S_e_e _A_l_s_o:

     'train', 'train.compare'

