MATLAB: Data replication Neural Networks Matlab

dataDeep Learning Toolboxneural networkoverfitpattern recognitionreplicate

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

Best Answer

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
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