Thanks very much for sharing.
I want to compile the code on Windows. However, it seems like the link given in "readme.txt"
doesn't work any more.
Could you tell me how to access the version for WINDOWS?
Thanks in advance
Thanks a lot for sharing this useful toolbox. I am trying to use chan_vase_model for my MRI data which are 3D but since contour showing is possible only for 2D case. I was wondering how can I set the parameters and be sure that the correct area has been selected ? i.e is there anyway to see how the code is actually working in 3D case?
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.