Best Validation check number for MATLAB neural network

I'm using 10-fold cross validation and patternent function for a binary classification problem in MATLAB. When I see neural network result window, in all trainings of neural network ( 80% training , 10% validation and 10% test with sample size 200~600 ) Early stopping is stopping my training process in iteration between 20~40. As you know the default value is maximum 6. What should i do about this problem? Should i increase maximum number of early stopping iteration checks?

Thanks.

Answer 1

[Greg Heath]

That is not necessarily a problem.

What error rates are you getting as you vary the number, H, of hidden nodes and sets of random initial weights?

I typically look at Ntrials = 10 different initial weight initializations for each candidate value of Hmin:dH:Hmax (numH~10).

Search in NEWSGROUP and ANSWERS

 greg patternet Ntrials

Hope this helps.

Thank you for formally accepting my answer

Greg

[Jack]

Thank you so much Greg for answer. I added some information here :

http://www.mathworks.com/matlabcentral/answers/152947-finding-best-neural-network-structure-using-optimization-algorithms-and-cross-validation