cross validation in neural network
i need some clarification on cross validation to be applied to neural network. i manage to get result of NN. right now i plan to apply cross validation for model selection.
i have go through example of *crossvalind, crossval* but i dont really understand what is classifier,in other word, what are the main things to be considered in order to apply cross validation.
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What do you mean by “model selection” … making a choice between newrb and fitnet(regression) or patternnet(classification)? Or, given one of them with one hidden layer, choosing the minimum number of hidden nodes that can achieve the design goal?
I do not have crossvalind and haven’t figured out how to use crossval for neural nets yet.
If I were in a hurry, I would just use randperm(N) to randomly divide the N cases of input/target pairs into 10 mutually exclusive subsets. Then use subset i (i=1:10), for testing, subset j (j ~= i) for validation and the remaining eight subsets for training. There is no need to shuffle data around because it can all be done with indexing.
With 10-fold XVAL there are 10*9 = 90 combinations for validation and test subset pairs. However, only 10 are needed.
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