similar to: logic question

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

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 everyone, I'm a graduate student of engineering, lately introduced with R. and using R for my project and thesis. I'm trying to use R for implementing a neural network regression model and apply it to my database. I found three R packages ("AMORE" , "grnnR" , "neural") in R website, but their manuals are not really user-friendly in my idea. I was wondering

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 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

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, 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

Hello, I am Mohiuddin Abdul Qader, final year student from dept of CSE in Bangladesh University of Engineering & Technology(BUET). My major was artificial intelligence & i finished my course on Machine Learning and Pattern Recognition this year. I am very keen to contribute in open source community. I have just completed my thesis on 'Location Based Structured Web Search'. For the

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

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

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

The TITLE for the nnet package is garbled: it comes out as nnet Feed-forward neural networks and multinomial log-linear nnet Feed-forward neural networks and multinomial log-linear models The problem is in maketitle: auk% cat DESCRIPTION Bundle: VR Version: 6.1-9 Date: 2000/07/11 Depends: R (>= 1.1) Author: S original by Venables & Ripley. R port by Brian Ripley

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]]

Dear All, I'm learning to train a neural network with my training data by using nnet package, then evaluate it with a evaluation set. My problem here is that, I need the trained network to be used in future, so, what should I store? and How? Any other options other than nnet package? Any example will be highly appreciated! Best, Baoqiang Cao

Hi, I am trying to make a neural network learning a "noisy sine wave". Suppose I generate my data like so.. x <- seq(-2*pi, 2*pi, length=500) y <- sin(x) + rnorm(500, sd=sqrt(0.075)) I then train the neural net on the first 400 points using c <- nnet(as.matrix(x[1:400]),as.matrix(y[1:400]), size=3, maxit=10000, abstol=0.075, decay=0.007) Inspecting the fit of the training

Greetings list, I've just swapped from the "neural" package to the "nnet" package and I've noticed that the training is orders of magnitude faster, and the results are way more accurate. This leads me to wonder, what training algorithm is "nnet" using? Is it a modification on the standard backpropagation? Or a completely different algorithm? I'm

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

Hello, I have a trained Back Propagation Neural Network model in weka. I would like to re-evaluate the NN using R with a given input. How can I do this? I could not find an example of RWeca that applies to NN Thanks, Rui

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