Hi, I used the nnet R module to classify my data using Neural Networks: nnet(input_matrix, obs_vect, size=h, linout=FALSE, entropy=TRUE) I used as NN input my "raw" data. After that I tried to use the normalized input data (with z-scores, i.e. mean=0 and std=1) and have found NNs with a little smaller Cross Entropy Error. My question is: Is it *wrong* to feed nnet directly with the raw input data?
I found in http://www.faqs.org/faqs/ai-faq/neural-nets/part2/section-16.html that it depends on the minimization training algorithm: - "Steepest descent is very sensitive to scaling. - Quasi-Newton and conjugate gradient methods... therefore are scale sensitive. However,... are less scale sensitive than pure gradient descent. - Newton-Raphson and Gauss-Newton, if implemented correctly, are theoretically invariant under scale changes..." I know that nnet is a Quasi-Newton algorithm, so it make sense that I found a small improvement using the normalized data. Can someone confirme if it is really so? Thank you very much! -- [EMAIL PROTECTED] ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
