Say we have N of labeled data, and we need to take some part for the
cross validation (we will skip
test part for this case). We chose, 0.6 part for the training and 0.4 for validation.
After training neural Network with
early stop, we have found 8 epochs, as optimal to stop, and have received good enough results.
Q. In case, we have very limited N training samples. May we use all samples in new model training, and just stop it's training after discovered epochs? Without separating it to
cross validation, and testing it, at all (or even, change rate of separating, to 0.9 train, 0.1 cross validation).
Maybe there is known technologies for such cases? Thanks.