i am confused with RBF kernal based ANN classification. when i implemented, all images are missclasified.. T is class label , P is training image feature , P1 is testing image feature...Can any one correct my code please,........................
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%%%%%%%% RBF training %%%%%%%%%%%
Tc=[1 1 1 1 2 2 2 2 2 2 2 2 2 ];
SPREAD=1;
T=ind2vec(Tc);
net=newrbe(P,T,SPREAD);
%%%%% test data %%%%%%%%
P1= FF1;
Y= sim(net,P1);
ANNresult = vec2ind(Y);
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Accepted Answer
Greg Heath
on 19 Feb 2015
P and FF1 are undefined
>> net = newrbe(P,T,SPREAD);
Undefined function or variable 'P'.
>> P1= FF1;
Undefined function or variable 'FF1'.
Hope this helps.
Thank you for formally accepting my answer
Greg
3 Comments
Greg Heath
on 23 Feb 2015
If P and P1 are fixed, there is only one way to improve your performance:
Vary SPREAD
Otherwise, redivide P+P1 and try again.
Hope this helps.
Greg
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