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lyngby_nn_qmain

(export/lyngby/lyngby_nn_qmain.m)


Function Synopsis

[V, W, EAcc, Info] = lyngby_nn_qmain(X, T, arg1, arg2, arg3, ...

Help text

 lyngby_nn_qmain      - Main function for quadratic neural network

	function [V, W, EAcc, Info] = lyngby_nn_qmain(X, T, ...
           'PropertyName', 'PropertyValue')

       Input:  X   Neural network input
               T   Target output
               Property:
                  'GenOptim'      { {Free} | EarlyStop | 
                                  HiddenUnitsEarlyStop | Pruning |
                                  1dreggridsearch | 2dreggridsearch
                                  Pruning1DRegGridSearch |
                                  Pruning2DRegGridSearch ]
                                  Generalization optimization
                  'Validation'    [ {SingleBlocked} ]
                                  How to compute the generalization
                                  measure
                  'TrainSet'      [ {not defined} | <indices> ]
                                  Indices for the training
                                  set. Default is all of X.
                  'ValSet'        [ {not defined} | <indices> ]
                                  Indices for the validation
                                  set. Only used in connection with
                                  "non-free" generalization
                                  optimization. Default is no
                                  validation set.
                   'HiddenUnits'  { 3 } Number of hidden units, not
                                  counting the threshold unit
                   'Reg'          { 0.001 } Regularization parameter
                                  (weight decay)
                   'Seed'         Seed for the random generator for
                                  the weights Matlab 4.x single seed
                                  generator is used. Default is no
                                  seed.
                   'Info'         [ {0} | 1 ] Continuous information
                                  about the optimization

       Output: V      Input weights
               W      Output weights
               EAcc   Evolution of error. Is dependent on the setting
                      of 'GenOptim'. Is scaled as: std(Y-T) / std(T),
                      where Y is the prediction for the validation
                      set and T is the target for the validation set.
               Info   Information about EAcc

       Main function for "quadratic neural network" that is the
       neural network with continuous regression output using least
       square fitting. 

       See also: lyngby_nn_emain, lyngby_nn_cmain, lyngby_nn_qtrain

Cross-Reference Information

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Produced by mat2html on Wed Jul 29 15:43:40 2009
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