Hi, We are trying to implement a early stopping rule with validation set on a neural network. We?re using the AMORE package (http://rwiki.sciviews.org/doku.php?id=packages:cran:amore) of R and when you train the network you have to specify following variables: Pval Tval What do we have to put here, or how do we have to specify this values? We are using simulated data from a sinc function. This is the code that we are using. #define a sinc function sinc <- function(x) sin(pi*x)/(pi*x) size_data = 200 # Generate data from sin function ticks = linspace(-1,1,size_data) sin_data = sinc(ticks) # Generate noise std_dev = 0.5 noise_data <- runif(size_data, 0, std_dev) # Impose noise on sin data dat = sin_data + noise_data #Normalise data max_dat = max(dat) norm_dat = dat/max(dat) #Define a neural network net.start <- newff(n.neurons=c(1,20, 1), learning.rate.global=1e-3, momentum.global=0.5, error.criterium="LMS", Stao=NA, hidden.layer="tansig", output.layer="purelin", method="ADAPTgd") #Train the network result <- train(net.start, ticks, norm_dat, Pval= NULL, Tval=NULL, error.criterium="LMS", report=FALSE, show.step=8000, n.shows=0) Are there any tips you can give for a better neural network or a better training of this net? Thanks a lot, A desperate team in search of help. -- View this message in context: http://n4.nabble.com/Neural-Network-tp1579365p1579365.html Sent from the R help mailing list archive at Nabble.com.