We are developing a neural network for classification there are 72 signals and there are 100 samples of each signal. We have extracted 49 parameters from each signal. What should my target matrix contain
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[ I N ] = size(input) [ c N ] = size(target)
The transformation between the true class index row vector and the corresponding target matrix is
target = full(ind2vec(trueclassind)) trueclassind = ind2vec(target)
Therefore, if the classifier output is
output = target+ randn(1,N)
the estimated classindex and error vectors are
estclassind = vec2ind(output) error = estclassind~=trueclassind Nerr = sum(error) PctErr = 100*Nerr/N
1. Test the above code with N = 9,
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