similar to: Neural Net. in R

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

I am interested in Neural network models in R. Is there any reference material/tutorial which i can use. Regards, ____________________________________________________________________________________ TV dinner still cooling? Check out "Tonight's Picks" on Yahoo! TV.

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 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

Does anyone know of any SOM library for R? or any stand alone freeware? A search from google returns SOM_PAK 3.1 developed at Helsinki University of Technology. Is there newer version? Jun -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or

hi all , has anyone tried to predict a univariate time series by a neural networks packages ? please help me in this problem . I am new in R and I did not found any document that explains this problem. thanks in advance David [[alternative HTML version deleted]]

I've put all ``current'' add-on packages into CRAN's source/contrib tree and created an INDEX file (attached below). As you can see, currently we have acepack bootstrap ctest date e1071 fracdiff gee jpn snns splines survival4 (Yes, e1071 and jpn are new ... more on the latter in a later mail.) In the near future, I am hoping for the following: oz (Bill

I am writing bindings to the neural network simulator SNNS. At present I have used only .C interface, now I'm studying .Call interface. I have adapted the example from page 77 of r-exts.pdf, however, it crashes R. I use MingW as recommended by Duncan Murdoch. Please, tell me what I am missing. The code is below. Thank you. SEXP snns_getVersion(void) {SEXP version; char *v;

I am writing bindings to the C library (neural network simulator SNNS). DLL was produced with the command R CMD SHLIB [source with C-wrappers, callable from R with .C and all sources from the snns kernel] I used latest MinGW. The problem is. R crashes with the "segmentation violation". Tracing with the insight and with simple Rprintf's gave me that crash occurs on the call of the

Hi, I want to use the neural network package AMORE and I don't find in the documentation the weight decay option. Could someone tell if it is possible to add a regularization parameter (also known as a weight decay) to the training method. Is it possible to alter the gradient descent rule for that? Thanks, Ron

I am using the iris example came with nnet package to test AMORE. I can see the outcomes are similar to nnet with adaptative gradient descent. However, when I changed the method in the newff to the batch gradient descent, even by setting the epoch numbers very large, I still found all the iris expected class=2 being classified as class=3. In addition, all those records in the outcomes (y) are the

Hi all, I am trying to develop a neural network with single target variable and 5 input variables to predict the importance of input variables using R. I used the packages nnet and RSNNS. But unfortunately I could not interpret the out put properly and the documentation of that packages also not giving proper direction. Please help me to find a good package with a proper documentation for neural

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

Are there any packages in R that are suitable for doing time series forecasting using neural networks? I have looked in the nnet package and neural package and they both seem geared towards classification. thanks, Spencer [[alternative HTML version deleted]]

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'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

Hello, I did a virsh snapshot-create-as --domain meta sn1 --diskspec vda,file=/var/lib/libvirt/images/meta-sn1.qcow2 --disk-only --atomic --no-metadata Then I successfully blockcommitted 2 disks: virsh blockcommit meta vda --active --verbose --pivot virsh blockcommit meta vdb --active --verbose --pivot But when doing virsh blockcommit meta vdc --active --verbose --pivot I got: Block commit:

Hi folks, I want to apply a neural network to a data set to classify the observations in the different classes from a concrete response variable. The idea is to prove different models from network modifying the number of neurons of the hidden layer to control overfitting. But, to select the best model how I can choose the relevant variables? How I can eliminate those that are not significant for

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,

I have copied the R FAQ pertaining to available packages into an EXCEL file. They take the following form - Design Regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit. Design is a etc. Devore5 Data sets and sample analyses from "Probability and Statistics for Engineering and the Sciences (5th