How to create target data for neural netwrok traing

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

Answer 1

[Greg Heath]

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

[roma marfatia]

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]

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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