MATLAB: About Neural Network….. How to train Neural Network

feature extractionimage processingneural networkspattern recognition

I am working on Pedestrian classification. I have data set having positive and negative images. I am using cohog as a feature extraction method. After Extracting features from both image sets I need to give them to Neural Network. Now here I have a confusion whether I have to mix the features of both pos and neg image set. Or train the NN individually. Kindly help me

Best Answer

The I input features of the N = N0+N1 point class mixture are put in the same input matrix
 [ I N ] = size(input)
For c =2 classes it is sufficient to use a binary 0/1 target vector
 [ O N ] = size(target)  % O = c-1 = 1
For a pattern recognition net with I-H-O node topology, there are
 Ntrneq = Ntrn*O  % e.g., Ntrn = N - 2*round(0.15*N) ~ 0.7*N
training equations to design
 net = patternnet(H);
using
 [ net tr ] = train(net,input,target);
The resulting net has
 Nw = (I+1)*H+(H+1)*O
unknown weights. Although it is desirable that
 H < -1 + ceil((Ntrneq-O)/(I+O+1)
so that Nw does not exceed Ntrneq, it is not necessary if validation set stopping (or regularization using msereg) is used.
The resulting net outputs
 y = net(input);
are interpreted as estimates of the class 1 posterior probabilty conditional on the input. The corresponding assigned class indices and error rates are given by
 class = round(y);
 Nerr1 = sum(class ~= 1)
 Nerr0 = sum(class ~= 0)
 PCTerr1 = 100*Nerr1/N1
 PCTerr0 = 100*Nerr0/N0
 PCTerr = 100*(Nerr1+Nerr0)/(N1+N0)
Hope this helps.
Thank you for formally accepting my answer
Greg
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