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Function Synopsis

[E, Y, VOut, WOut] = lyngby_nn_ctrain(X, T, VOld, WOld, Reg, ...

Help text

 lyngby_nn_ctrain     - Classifier neural network training 

	function [E, Y, VOut, WOut] = lyngby_nn_ctrain(X, T, VOld, ...
	          WOld, Reg, 'PropertyName', 'PropertyValue')

	Input:	X       Neural network input
               T       Target output 
		VOld    Old input weights
		WOld    Old output weights
               Reg     Regularization (weight decay)
                 MaxIteration  {200} Iteration stop criterion
                 MinCost       {0} Iteration stop criterion
                 MinGradient   {10^(-7)} Iteration stop criterion
                 Method        Optimization type
                 WeightAcc     [ {off} | on ] Accumulate weights
                 Info          [ {0} | 1 ] Reporting of
                               costfunction and gradient. Zero
                               means off

	Output:	E	Entropic error (cost without regularization)
		Y	Computed Outputs
		WOut	New trained output weights or accumulated
		        weights (depending on 'WeightAcc')
		VOut	New trained hidden weights or accumulated
		        weights (depending on 'WeightAcc') 

       This function trains a neural network, either pruned or fully
       connected. It will continue until one of the stop criterions
       are meet: maxIteration is the number of epochs (optimization
       steps), minCost is the highest acceptable value for the cost
       function, minGradient is the hightest acceptable value for the
       norm of the gradient.

Cross-Reference Information

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Produced by mat2html on Wed Jul 29 15:43:40 2009
Cross-Directory links are: OFF