Question
Hello world.! I have recently been studying neural networks, so I may ask something obvious, but I figured out that when I replicate my inputs and outputs and then train the network for pattern recognition,it has far more accuracy than with the original data. I thought of that in order to replicate some of the extreme values I have. Can that make my network overfit? Thank you everyone
Expert Answer
Neeta Dsouza
PhD Expert
Answered Sep 15, 2026
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
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