How to create target data for neural netwrok traing

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roma marfatia
roma marfatia el 15 de Abr. de 2014
Comentada: Greg Heath el 23 de Abr. de 2014
i have training data, 8 samples, where each sample contains 32 features, and total number of class in which they should fall are 8 classes. how should i create my input and target data

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Greg Heath
Greg Heath el 16 de Abr. de 2014
1. Nomenclature: You have one sample. The sample contains N=8, I=32-dimensional input examples from c=8 classes.
2. Are they all from different classes or are there empty classes and classes with multiple members?
3. 8 examples define, at most, a 7-dimensional input space. Therefore, if you cannot get more training examples, you should reduce the dimensionality to, at most, 7.
4. The input and target matrices should have the sizes
[ I N ] = size(input)
[ c N ] = size(target)
where the target columns are columns of the unit matrix eye(c).
5. Ideally you would like N >~ 15*I. So find more data. Otherwise use a simple classifier like linear or nearest neighbor.
Hope this helps.
Thank you for formally accepting my answer
Greg
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roma marfatia
roma marfatia el 20 de Abr. de 2014
hey greg, i am not able to understand, what you have told. i am using neural network as an classifier. let suppose i m having 3 objects (apple, mango, grapes). now my training data is of size 3 X 30 , i.e. 3 samples each containing 30 features. now what will be my target data? please help me as soon as possible. THANKYOU !!! waiting for your response...
Greg Heath
Greg Heath el 23 de Abr. de 2014
Again: You only have ONE sample of data. You have 3 classes with 1 30-dim example in each class. 3 examples define a 2-dimensional space. Therefore you should use dimensionality reduction and/or try to find more examples. Your design example is really too simple for any thing except a linear, quadratic or nearest neighbor classifier.
For more appropriate design examples see the MATLAB data base
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