1. I don't understand your question.
2. a. OVERFITTING means there are more unknown weights, Nw, than independent training equations, Ntrneq ( i.e., Nw > Ntrneq).
b. OVERTRAINING an overfit net CAN LEAD to loss of performance on NONTRAINING data.
3. There are several remedies to prevent OVERTRAINING AN OVERFIT NET. So, in general, overfitting need not be disastrous.
4. Methods for preventing loss of generalization via overtraining an overfit net
a. Do not overfit: Nw < Ntrneq. Preferrably,
Ntrneq >> Nw which yields design Stability and
robustness w.r.t. noise and measurement error.
For example:
i. Increase the number of training examples
ii. Reduce the number of hidden nodes
b. Use VALIDATION STOPPING to prevent overtraining
c. Use the BAYESIAN REGULARIZATION
training function TRAINBR with MSEREG
as a default.
d. Replace the default performance function
MSE with the regularized
modification MSEREG
Hope this helps.
Thank you for formally accepting my answer
Greg
