I used this toolbox successfully with Matlab R2013a. It is very beneficial to have the book referenced in the above description. My application was classification of sounds in a trained NN into one of several categories. here are a few notes from my specific application:
Activation Functions Investigated
Linear – simplest, gives good results
Softmax – best general purpose for 1 of N classification
Logistic – good for binary classifications
Conjugate gradient descent – worst performing method
Scaled conjugate gradient descent (SCG) – sometimes superior
Quasi-Newton – gives most consistent results for current data set
Search for best number of hidden units
Smaller number runs faster/simpler
Larger number may provide more accurate results with the possibility of over-fitting the available data
Current data set, with 4 possible sound classifications, gave best result with about 15 hidden units
I also tried using a support vector machine for the same application and it performed slightly better.
can you help me out in it ... i am using SVM quadratic classifier; it returns class labels for test samples (i.e. 1(pos) or -1(neg)); how can i obtain score values for test samples to plot PR and ROC curves? Thanks a lot for the upload!