Hi there,
I have a few general questions about data classification using neural networks.
I've been "playing around" with different networks such lvq and the nprtool, without really knowing what is the best practice and understanding what is actually happening.
I have 3000 input vectors that I can use for training. They are 27×1 matrices. And I have 9 target vectors.
A few general questions that I have:
1. What is the best network to use generally for data classification, lvq, the nprtool or some other network that I'm not aware of?
2. Is it necessary to have the same number of input vectors per target during training like 333 input vectors for each target (333×9=2997)? Or would having for example 5 times more input vectors of one target result in a bad network?
2. Is 9 target vectors too many?
3. What is the rule of thumb for the number of hidden neurons? Roughly how many should I have?
4. Should I use a particular type of learning function?
5. The 27 inputs are measurements, some with different units, I normalized all inputs between 0 and 1, is that a good thing to do?
I would appreciate any comments and suggestions as I have not been able to find these answers online.
Many thanks
John
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