similar to: classification task with RBF neural networks

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,

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

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 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

Dear list, I am new to R and am using it to develop and test my own neural network codes. I need some training datasets that have the prediction results that should (approximately) appear when the datasets are passed through a good neural network, in order to test whether my code is working according to standards or not. Currently I am using nnet() and predict() function in nnet package to

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 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 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

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

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

Hi all gurus, I have a few general questions about using neural networks function, i.e. the nnet function. I'm new to this function and still exploring it. So please kindly bear with me. Here are my questions. 1. Is there anyway that I can specify my own objective or loss function for my neural networks? I see that by arguments that we can pass into the networks, we have either square loss

Dear R-list! I am using the e1071 package in R to solve a binary classification problem in a dataset of round 180 predictor variables (blood metabolites) of two groups of subjects (patients and healthy controls). I am confused regarding the correct way to assess the classification accuracy of the trained svm. (A) The svm command allows to specificy via the 'cross=k' parameter to specify a

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, Dear R community, I am using the nnet to fit a neural network model to do classification on binary target variable (0, 1). I am using the following codes: nnet.fit<-nnet(as.factor(out) ~ ., data=train, size=5, rang=0.3, decay=5e-4, maxit=500) I want to know what is the activation function for the original inputs, is it sigmoid activation function? and what is the output activation

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

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

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 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]]

Hi, I am learning CNN using MXNet R package. I am following this great tutorial about olivetti_faces reconnaissance <https://www.r-bloggers.com/image-recognition-tutorial-in-r-using-deep-convolutional-neural-networks-mxnet-package/>. In the end after building model and testing the final score was 0.975. It is great score but what can do after with this model? How can use this model for

Hello list, I am faced with a two-class classification problem with highly asymetric class sizes (class one: 99%, class two: 1%). I'd like to obtain a class probability model, also introducing available information on the class prior. Calling kernlab/ksvm with the line > ksvm_model1<-ksvm(as.matrix(slides), as.factor(Class), class.weights= c("0" =99, "1" =1),