similar to: Optimize nnet() for cross-validation error

Displaying 20 results from an estimated 30000 matches similar to: "Optimize nnet() for cross-validation error"

2007 Jul 23
4
nnet 10-fold cross-validation
Hi It clear that to do a classification with svm under 10-fold cross validation one uses svm(Xm, newlabs, type = "C-classification", kernel = "linear",cross = 10) What corresponds to the nnet? nnet(.....,cross=10)? Regards
2005 Aug 26
1
passing arguments from nnet to optim
Hi everyone, According to R reference manual, the nnet function uses the BFGS method of optim to optimize the neural network parameters. I would like, when calling the function nnet to tell the optim function not to produce the tracing information on the progress of the optimization, or at least to reduce the frequency of the reports. I tried the following: a) nnet default > x<-rnorm(20)
2009 Jun 07
1
Inf in nnet final value for validation data
Hi, I use nnet for my classification problem and have a problem concerning the calculation of the final value for my validation data.(nnet only calculates the final value for the training data). I made my own final value formula (for the training data I get the same value as nnet): # prob-matrix pmatrix <- cat*fittedValues tmp <- rowSums(pmatrix) # -log likelihood
2012 May 30
1
caret() train based on cross validation - split dataset to keep sites together?
Hello all, I have searched and have not yet identified a solution so now I am sending this message. In short, I need to split my data into training, validation, and testing subsets that keep all observations from the same sites together ? preferably as part of a cross validation procedure. Now for the longer version. And I must confess that although my R skills are improving, they are not so
2010 Jan 29
0
Help interpreting libarary(nnet) script output..URGENT
Hello, I am pretty new to R. I am working on neural network classifiers and I am feeding the nnet input from different regions of interest (fMRI data). The script that I am using is this: library (MASS) heap_lda <- data.frame(as.matrix(t(read.table(file="R_10_5runs_matrix9.txt")))*100000,syll = c(rep("heap",3),rep("hoop",3),rep("hop",3))) library(nnet)
2010 Jun 08
2
cross-validation
Hi   I want to do leave-one-out cross-validation for multinomial logistic regression in R. I did multinomial logistic reg. by package nnet in R. How I do validation? by which function? response variable has 7 levels   please help me   Thanks alot Azam [[alternative HTML version deleted]]
2003 Aug 19
3
On the Use of the nnet Library
Dear List, I am trying to solve a problem by the neural network method(library: nnet). The problem is to express Weight in terms of Age , Sex and Height for twenty people. The data frame consists of 20 observations with four variables: Sex, Age, Height and Weight. Sex is treated as a factor, Age and Weight are variables normalized to unity, as usual. I wanted to construct a neural network, and so
2007 Jan 28
2
nnet question
Hello, I use nnet to do prediction for a continuous variable. after that, I calculate correlation coefficient between predicted value and real observation. I run my code(see following) several time, but I get different correlation coefficient each time. Anyone know why? In addition, How to calculate prediction accuracy for prediction of continuous variable? Aimin thanks, > m.nn.omega
2011 Jan 05
0
Nnet and AIC: selection of a parsimonious parameterisation
Hi All, I am trying to use a neural network for my work, but I am not sure about my approach to select a parsimonious model. In R with nnet, the IAC has not been defined for a feed-forward neural network with a single hidden layer. Is this because it does not make sens mathematically in this case? For example, is this pseudo code sensible? Thanks in advance for your help. I am sorry if this
2003 Sep 30
1
NNet value and convergence
Hi, I'm using the R nnet package and have a few simple (?) questions. What is the "value " that is output after every 10 iterations during the training of the network and how is it calculated? # weights: 177 initial value 506.134586 iter 10 value 128.222774 iter 20 value 95.399782 iter 30 value 87.184564 ... Is the "value" the error, if not, is there any way
2012 Sep 21
0
using neural network in R (nnet)
Hi all, I have considered neural network to classify the health status of the cow. I found a very neatly written R codes for classification method in here<http://home.strw.leidenuniv.nl/~jarle/IAC/RRoutines/classification-example.R> . It would be very helpful if you can answer some of the questions, that I am struggling with, I have set of time series data from different animals, I use
2010 Oct 21
1
Accuracy/Goodness of fit of nnet
Hi R-Helpers , am working on nnet package.Multinom() has an option for finding the goodness of fit by giving the AIC value. Does nnet also gives some value to determine the accuracy. If not, can you guide me with some procedure to figure out the accuracy/goodness of fit of nnet model? Thanks in advance. -- View this message in context:
2010 Nov 03
0
bad optimization with nnet?
Hy, I try to give an example of overfitting with multi-layer perceptron. I have done following small example : library(nnet) set.seed(1) x <- matrix(rnorm(20),10,2) z <- matrix(rnorm(10),10,1) rx <- max(x)-min(x) rz <- max(z)-min(z) x <- x/rx z <- z/rz erreur <- 10^9 for(i in 1:100){ temp.mod <- nnet(x=x,y=z,size=10,rang=1,maxit=1000) if(temp.mod$value<erreur){
2000 Aug 02
0
? predict.nnet
Hi, I just want to point out a discrepancy between the documentation of predict.nnet & the function definition. >?predict.nnet => predict.nnet package:nnet R Documentation Predict New Examples by a Trained Neural Net Description: Predict new examples by a trained neural net. Usage: predict.nnet(object, x,
2010 Dec 10
2
Need help on nnet
Hi, Am working on neural network. Below is the coding and the output > library (nnet) > uplift.nn<-nnet (PVU~ConsumerValue+Duration+PromoVolShare,y,size=3) # weights: 16 initial value 4068.052704 final value 3434.194253 converged > summary (uplift.nn) a 3-3-1 network with 16 weights options were - b->h1 i1->h1 i2->h1 i3->h1 16.64 6.62 149.93
2006 Mar 23
0
front- end problem while using nnet and tune.nnet
Dear R people, I am using tune.nnet from e1071 package to tune the parameters for nnet. I am using the following syntax: tuneclass <-c(rep(1,46),rep(2,15)) tunennet <-tune.nnet(x=traindata,y=tuneclass,size=c(50,75,100),decay=c(0,0.005,0.010),MaxNWts = 20000) Here traindata is the training data that I want to tune for nnet which is a matrix with 61 rows(samples) and 200
2004 Aug 01
1
Neural Net Validation Sub Set
Dear R users, I have been playing with the nnet and predict.nnet functions and have two questions. 1) Is it possible to specify a validation set as well as a training set in the nnet function before using predict.nnet to test the nnet object against new data? 2) Is it possible to specify more than one layer of neurons? Thanks in advance Matt Oliver
2006 Dec 21
1
multinom(nnet) analogy for biglm package?
I would like to perform a multinomial logistic regression on a large data set, but do not know how. I've only thought of a few possibilities and write to seek advice and guidance on them or deepening or expanding my search. On smaller data sets, I have successfully loaded the data and issued commands such as: length(levels(factor(data$response))) [1] 6 # implies polychotomy library(nnet)
2006 Dec 03
1
nnet() fit criteria
Hi all, I'm using nnet() for non-linear regression as in Ch8.10 of MASS. I understand that nnet() by default optimizes least squares. I'm looking to have it instead optimize such that the mean error is zero (so that it is unbiased). Any suggestions on how this might be achieved? Cheers, Mike -- Mike Lawrence http://artsweb.uwaterloo.ca/~m4lawren "The road to wisdom? Well,
2005 Sep 09
0
usage of the trianed networks by nnet without R enviromen t
One possibility is to look at predict.nnet(), and - Write an R function that write out parts of an nnet object that are needed by predict.nnet() to an external file. - Re-write predict.nnet() in C, reading the model information from the external file. Obviously you'll also need the C source for the code that predict.nnet() calls, and modify those as needed to strip out dependency on R, if