similar to: Artificial 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 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 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 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]]

Dear community, I have a problem with a string conversion: > text [1] "" "and" "\xc1d\xe1m" [4] "graphical" "interface" "MLP" [7] "Nagy" "networks" "Networks" [10] "neural" "Neural"

Hi R-helpers, Can some one tell me how to train 'mynn' of this type?: mynn <- nnet(y ~ x1 + ..+ x8, data = lgist, size = 2, rang = 0.1, decay = 5e-4, maxit = 200) I assume that this nn is untrained, and to train I have to split the original data into train:test data set, do leave-one-out refitting to refine the weights (please straighten this up if I was wrong). I just don't know

Hello everybody, I'm looking for a way to build an RBF classification network with R but I can't find any. I know there is the 'neural' package, but apparently the RBF networks I can build with that are for approximation tasks only. Is there any package I can use to build an RBF network for a classification task? I've also looked on CRAN but couldn't find any. Thank you for

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

Hi all, sorry for my english, but I don't speak yours language. I'm trying to use bart() and rbf(). The package I'm using now is "BayesTree" and "neural", respectively. I could create the models, but I can't predict my test data. Does anyone have such an experience? Any advice is appreciated! Thank you in advanced!. Andr? -- View this message in

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, 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

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,

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

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

Hi, I am would like to ask few questions. I am trying to forecast hourly electricity prices by 24 hours ahead. I have hourly data starting from 2015*12*18 to 2017-10-24 and I have defined the data as time series as written in the code below. Then I am trying do neural network with 23 non-seasonal dummies and 1 seasonal dummy. But I don?t know whether training set is enough.( Guess it is 50

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

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 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