similar to: Variables selection in Neural Networks

Hi All, I am trying to learn Neural Networks. I found that R has packages which can help build Neural Nets - the popular one being AMORE package. Is there any book / resource available which guides us in this subject using the AMORE package? Any help will be much appreciated. Thanks, Indrajit

Hi. I started with a file which was a sparse 982x923 matrix and where the last column was a variable to be predicted. I did principle component analysis on it and arrived at a new 982x923 matrix. Then I ran the code below to get a neural network using nnet and then wanted to get a confusion matrix or at least know how accurate the neural net was. I used the first 22 principle components only for

I want to create a neural network, and then everytime it receives new data, instead of creating a new nnet, i want to use a backpropagation algorithm to adjust the weights in the already created nn. I'm using nnet package, I know that nn$wts gives the weights, but I cant find out which weights belong to which conections so I could implement the backpropagation algorithm myself. But if anyone

Hi, I am an old hand at chemistry but a complete beginner at statistics including R computations. My question is whether you can carry out nonlinear multivariate regression analysis in R using neural networks, where the output variable can range from -Inf to + Inf., unlike discriminant analysis where the output is confined to one or zero. The library nnet seems to work only in the latter

Hi I would like to know how I can change the name of a model for each trainning cycle of a model. I work with the RSNNS package and to build a neural network, I used : for (i in 5:30) .... model_ANN <- mlp(X, Y, size=n,....) # where size is the number of neurons in the hidden layer but I need to save each time that the model that is build (the end of each cycle), e.g., when i = 5, I need to

Hello all, Ive been playing with nnet (package 'nnet') and Ive come across this problem. nnet doesnt seems to like to have more than 1000 weights. If I do: > data(iris) > names(iris)[5] <- "species" > net <- nnet(species ~ ., data=iris, size=124, maxit=10) # weights: 995 initial value 309.342009 iter 10 value 21.668435 final value 21.668435 stopped after 10

Hi, I am trying to build a VERY basic neural network as a practice before hopefully increasing my scope. To do so, I have been using package "neural" and the MLP related functions (mlp and mlptrain) within that package. So far, I have created a basic network, but I have been unable to change the default activation function. If someone has a suggestion, please advise. The goal of the

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

Hi, I'm currently trying desperately to get the nnet function for training a neural network (with one hidden layer) to perform a regression task. So I run it like the following: trainednet <- nnet(x=traindata, y=trainresponse, size = 30, linout = TRUE, maxit=1000) (where x is a matrix and y a numerical vector consisting of the target values for one variable) To see whether the network

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

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

Hi all, I'm running a monte carlo test of a neural network tool I've developed, and it looks like it's going to take a very long time if I run it in R so I'm interested in translating my code (included below) into something faster like Fortran (which I'll have to learn from scratch). However, as you'll see my code loads the nnet library and uses it quite a bit, and I

Hi all I have a question about correct usage of persp(). I have a simple neural net-based XOR example, as follows: library(nnet) xor.data <- data.frame(cbind(expand.grid(c(0,1),c(0,1)), c(0,1,1,0))) names(xor.data) <- c("x","y","o") xor.nn <- nnet(o ~ x + y, data=xor.data, linout=FALSE, size=1) # Create an (x.y) surface and predict over all points d <-

Hi! I am studying to use some R libraries which are applied for working with artificial neural neworks (amore, nnet). Can you recommend some useful, reliable and easy to get example data to use in R for creating and testing a neural network? __________________________________________________________________ Make your browsing faster, safer, and easier with the new Internet

Take a look at package mgcv. Hope this helps. --Matt -----Original Message----- From: RenE J.V. Bertin [mailto:rjvbertin at despammed.com] Sent: Thursday, February 27, 2003 1:39 PM To: r-help at stat.math.ethz.ch Subject: [R] multidimensional function fitting Hello, I have been looking around for how to perform a multidimensional, arbitrary function fit (in any case non-linear; more below),

Hi, I am starting to play around with neural networks and noticed that there are several packages on the CRAN website for neural networks (AMORE, grnnR, neural, neuralnet, maybe more if I missed them). Are any of these packages more well-suited for newbies to neural networks? Are there any relative strengths / weaknesses to the different implementations? If anyone has any advice before I dive

Hi, I'm working on a classification problem and one of the methods I'd like to use are neural networks. I've been using nnet to build a classification network. However I would like to have the probabilities associated with the prediction. Are there any implementations of probabilistic neural networks available in R? thanks,

Hello! We are a group of three students at Bielefeld University currently working on a statistical projects about neural networks. Within the framework of this project we are supposed to use the nnet-function in R and explain how it works. Since anyone of us has much experience in using R we hoped to find some information on your homepage. Unfortunatelly, we haven't been very successfull so

Dear R users, I'd like to ask your guidance regarding the following two questions: (i) I just finished reading Chris Bishop's book "Neural Networks for Pattern Recognition". Although the book gave me good theoretical foundation about NN, I'm now looking for something more practical regarding architecture selection strategies. Is there any good reference about "best

Sorry for these multiple postings. I solved the problem using na.omit() to drop records with missing values for the time being. I will worry about imputation, etc. later. I calculated the sum of squared errors for 3 models, linear regression, neural networks, and support vector machines. This is the first run. Without doing any parameter tuning on the SVM or playing around with the number of