Cross validation neural networks MATLAB
FAQs : What are cross-validation and bootstrapping?
In k-fold cross-validation, you divide the data into k subsets of
(approximately) equal size. You train the net k times, each time leaving out one of the subsets from training, but using only the omitted subset to compute whatever error criterion interests you.
If k equals the sample size, this is called "leave-one-out" cross-validation. "Leave-v-out" is a more elaborate and expensive version of cross-validation that involves
leaving out all possible subsets of v cases.
Split Sample or Hold Out
cross-validation is quite different from the "split-sample" or "hold-out" method that is commonly used for early stopping in NNs. In the split-sample method, only a single subset (the validation set) is used to estimate the generalization error, instead of k different subsets; i.e., there is no "crossing".
While various people have suggested that cross-validation be applied to early stopping, the proper way of doing so is not obvious.
The rest of the document is interesting - it defines and discusses Jackknifing and Bootstrapping.
MATLAB neural network manual does not use either terms (as far as I can see,
Source: tumbling ideas
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