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Todd Watson

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Illinois Tool Works

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21 Mar 2014 Netlab Pattern analysis toolbox. Author: Ian Nabney

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

Optimization Methods
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.
http://www.esat.kuleuven.be/sista/lssvmlab/downloads/tutorialv1_8.pdf

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