MATLAB: How to define target matrix for rbf neural classifier

classifiersrbftrain

i have 10 original images and 10 corrupted images. the image features are in 10 X 548 matrices. i want to classify the original images and corrupted images separately. so i should classify the images as '1' if original image and classify as '0' if corrupted image. 1. how to define the target matrix? 2. what values to use for goal and spread in newrb? 3. how to simulate the network?

Best Answer

target = [ ones(1,10) zeros(1,10)]

MSE00 = mean(var(target',1))

MSEgoal = MSE00/100

Standardize inputs (ZSCORE or MAPSTD) and search for spreads about unity

spread(1) = 1

spread(i+1) = 0.5*spread(i)

and

spread(j+1) = 2*spread(j)

Hope this helps

Greg

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 1. Ntrn ~  0.7*651 ~  456 
 2. Use Ntrn for significant correlation threshold estimates
 3. 1000 repetitions seems extreme. How much difference if 
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 4. Mislabeled crosscorr as autocorr
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Hope this helps.

Thank you for formally accepting my answer*

Greg

MATLAB: How to get MSE and normalized MSE both as performance function when fitting Feed forward neural network

Divide MSE by the mean variance of the target rows (MSE of the constant output model)

 MSEref = mean(var(target',1))
 NMSE = mse(target-output)/MSEref
 Rsquare = 1 - NMSE

Hope this helps.

Thank you for formally accepting my answer

Greg

Best Answer

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MATLAB: How to get MSE and normalized MSE both as performance function when fitting Feed forward neural network

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