>I want to use a RBFNN for character recognition. I have 40 images >stored in a input vector N and 40 targets stored in a target vector T >which I supplied as the first two arguments of the function newrb >(net = newrb(N,T)
1. You erroneously use the term "vector" instead of "matrix".
2: Please use N for the number of input/target vector pairs
3. NEWRB, NEWRBE & NEWGRNN are ancient RBFNN functions that are inferior in flexibility to the current curvefitting and pattern recognition functions FITNET and PATTERNNET, respectively.
a. In particular, THE FORMER ARE NOT EQUIPPED TO
AUTOMATICALLY DEAL WITH DATA DIVISION AND
VALIDATION STOPPING.
b. Although the latter 2 automatically deal with
data division, when used with RADBAS functions, the
results are ELLIPTICAL bASIS FUNCTIONS (EBFNNs)!
4. Input data to MATLAB NN functions should be stored in two separate 2-D MATRICES.
5. Each of the N 2-D input images should be either a. vectorized into an I-dimensional vector (use colon operator) or b. represented by extracted features which are stored in a single I-dimensional vector
6. Each of the N I-dimensional "I"nput vectors corresponds to an O-dimensional "O"utput target vector that, for pattern-recognition and classification, is a unit vector with a single one ("1") and O-1 zeros ("0").
7. The input and target matrices have the dimensions [ I N ] = size(input) [ O N ] = size(target)
8. For pattern-recognition, the target vectors are columns of a (0,1) unit vector . The input vector is assigned to the class corresponding to the row of the output vector that has the highest value.
> and now I want to evaluate the performance of the RBFNNs ability > to recognise patterns using a train, validation and test but I do not > know how ... I wanted to divide up my data into these three sets > using the dividerand function.
9. Again, there is no efficient way to use validation stopping datadivision with the above RBFNNs. I suggest using PATTERNNET EBFNNs.
PATTERNNET allows several automated data division functions. DIVIDERAND is the default and does not have to be explicitly specified.Specifying RADBAS functions in the hidden layer will result in EBFNNs.
References
http://www.mathworks.com/matlabcentral/newsreader/view_thread/304783#948573 http://www.mathworks.com/matlabcentral/newsreader/view_thread/315697#862831 http://www.mathworks.com/matlabcentral/newsreader/view_thread/151286#380974
HOPE THIS HELPS.
Thank you for formally accepting my answer
GREG
