which is the best neural network for classification problem?

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Dhandapani.S
Dhandapani.S 2015-1-25
评论： Greg Heath 2015-1-26
for 2-class classification problem, which is the best neural network, what transfer function to use and the number of desired number of neurons?
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Image Analyst
Image Analyst 2015-1-25
I imagine that Greg would say "It depends, and you have not supplied enough information to even give an answer", so I would recommend you read this and fix your post.

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Greg Heath
Greg Heath 2015-1-25
It depends.
However, in general, the default should be the standard classification version of the universal approximator. The MATLAB version is PATTERNNET.
help patternnet
doc patternnet
For practice data
help nndatasets
doc nndatasets
For nontrivial examples search the NEWSGROUP and ANSWERS using
greg patternnet
Hope this helps.
Thank you for formally accepting my answer
Greg
  2 个评论
Dhandapani.S
Dhandapani.S 2015-1-25
i am working on handwritten signature verification modelling. i extract features of the signature andtryinh to use neural network for clasification. wht should be the target value. i have ketp 1 for genuine and 0 for forged signatures. what will be the best training trsting ratio?
Greg Heath
Greg Heath 2015-1-26
Total = design + test
design = training + validation
nontraining = validation + test
The ratio is not important. The most important thing is to have enough training examples to design an accurate net that is robust with respect to noise, interference and transcription errors.
For an I-H-O node topology
Ntrneq = Ntrn*O % No. of training equations
Nw = (I+1)*H+(H+1)*O % No. of unknown weights
For robustness desire
Ntrneq >> Nw <==> H << -1+ceil( (Ntrneq-O) / (I+O+1) )
Otherwise consider validation stopping and/or regularization (msereg and/or trainbr).
Hopefully, there is enough left to have reasonably precise estimates on
a. nontraining test data
b. nondesign validation data
If not, you can resort to regularization instead of validation and/or multiple crossvalidation for precise test estimates.
Typically, my training goal is
Minimize H subject to mse(target-output) < 0.01*mean(var(target',1))
Then the net successfully models 99% of the target variance.
Hope this helps.
Greg

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