> let's say I have a network created using newrb using a particular training data set. I have checked it on validation data set and obtained certain results. (split sample validation)
There is a misuse of the terminology VALIDATION. With NNs, the
val set is used to
a. Stop training when the val subset performance decreases for a
specified number of epochs.
b. Rank multiple designs.
> Now I would like to perform cross-validation over the whole amount of dataset. But obviously I cannot use newrb again, since it will generate another NN topology, while I would like to test the topology that was created in the first step.
That makes no sense. With NEWRB the topology changes whenever the training set changes.
> I have the following sequence of actions:
> 1. create newrb using test data set - only training data data
That makes no sense.
> 2. retrain it using train function using the whole data set
split using crossvalind - training+validation mixed for
cross-validation
That makes no sense because NEWRB is not created using an
external training function.
> PROBLEM: while validation using the first approach (split sample validation) does not give good results, second approach gives even higher errors
> QUESTION: Does it happen due to the sensitivity of rbfn to the number of samples in the dataset? Are there any other ways to perform cross validation of RBFN network?
> thank you in advance! Alexandra
Randomly divide the data into k equal (as much as possible)
subsets.
for i = 1:k
if i > 1, Replace subset i-1, end
1. Use the ith subset for a nontraining test subset
2. Design with the remaining k-1 subsets
3. Test with the ith subset
end
Average the k results.
This can be repeated as many times as desired and the results averaged.
Hope this helps.
Thank you for formally accepting my answer
Greg
