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Topic: CROSSVALIDATION TRAINING FOR NEURAL NETWORKS
Replies: 1   Last Post: Feb 15, 2013 4:27 PM

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 Greg Heath Posts: 214 Registered: 12/13/04
Re: CROSSVALIDATION TRAINING FOR NEURAL NETWORKS
Posted: Feb 15, 2013 4:27 PM

On Feb 15, 4:08 pm, "Greg Heath" <he...@alumni.brown.edu> wrote:
> Although f-fold XVAL is a very good and popular way for designing and
> testing NNs, there is no MATLAB NNTBX function for doing so. I have
> done it for classification and regression by brute force using do loops.
> Unfortunately, that code is not available since my old computer crashed.
> Nevertheless, the coding was straightforward because random weights
> are automatically assigned when the obsolete functions NEWPR, NEWFIT
> or NEWFF are created. With the current functions FITNET, PATTERNNET
> and FEEDFORWARDNET, you either have to use a separate step with
> CONFIGURE or let the TRAIN function initialize the weights.
>
> I did add a few modifications. With f >=3 partitions there are F =
> f*(f-1)/2 ( = 10 for f = 5) ways to choose a holdout nontraining pair for
> validation and testing. So, for each of F (=10) nets I trained untill
> BOTH holdout set errors were minimized. I then obtained 2 nontraining
> estimates for error: The error of holdout2 at the minimum error of
> holdout1 and vice versa. Therefore for f = 5, I get 2*F = 20 holdout
> error estimates which is a reasonable value for obtaining
> min/median/mean/std/max (or even histogram) summary statistics.
>
> However, I did get a query from one statistician who felt that somehow
> the multiplication factor of F/f = (f-1) ( = 4 for f=5) is biased. The
> factor of f-1 is from the use of  f-1 different validation sets for each
> of f test sets. However, the f-1 different validation sets correspond to
> f-1 different training sets, so I don't worry about it.
>
> In addition, for each of the F nets you can run Ntrials different weight
> initializations. So, for f = 5, Ntrials = 5 you can get F*Ntrials = 100
> error estimates.
>
> With timeseries, preserving order and uniform spacing is essential. I
> have only used f-fold XVAL with f = 3 and either DIVIDEBLOCK or
> DIVIDEINT types of data division. In the latter case the spacing is
> tripled and success will depend on the difference of the significant
> lags of the auto and/or cross correlation functions. The trick of two
> holdout error estimates per net still works. Therefore, you can get
> 2*Ntrials error estimates. I can't see any other way to preserve
> order and uniform spacing.
>
> The MATLAB commands
>
> lookfor crossvalidation
> lookfor 'cross validation'
> lookfor validation
>
> may yield functions from other toolboxes which may be of use.
> However I think the use of 2 holdout nontraining subsets is
> unique to neural network training.
>
> Hope this helps.
>
> Greg

Date Subject Author
2/15/13 Greg Heath
2/15/13 Greg Heath