If you read the reference on the help page, you will find out the answer. After all, as the DESCRIPTION says, this is support software for a book.
On Wed, 16 Jun 2004, [EMAIL PROTECTED] wrote: > 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? -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ [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
