similar to: Neural Network

Hola a todos, en primer lugar quería agradecer la ayuda recibida desde el foro con respecto a la creación de una red neuronal. Estoy utilizando el paquete Neuralnet, que me parece que es bastante bueno, pero tengo el problema que soy incapaz de hacer las predicciones del modelo. Sé que se hace con el comando "compute", pero no encuentro ningún ejemplo de cómo hacerlo. ¿Alguien me puede

> Dear list, > > I have some data on a geneaology, here is a subset: > warmerge[1:11,c(1,6,25)] > Warrior SibID birth.year > 1100 3793 2013 1926 > 4 2013 2024 1934 > 1094 3769 2024 1918 > 632 2747 2037 1928 > 176 2083 2039 1944 > 187 2085 2039 1949 > 192 2086 2039 NA > 495

Dear list, I am fitting a logistic multi-level regression model and need to test the difference between the ordinary logistic regression from a glm() fit and the mixed effects fit from glmer(), basically I want to do a likelihood ratio test between the two fits. The data are like this: My outcome is a (1,0) for health status, I have several (1,0) dummy variables RURAL, SMOKE, DRINK, EMPLOYED,

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

can u explain me, how it works your code??? please. i´m also doing a simple perceptron for homework on R and i dont know where to start. [[alternative HTML version deleted]]

Hello R masteRs, I was wondering if somebody had already implemented a parallel version of the function Nnet (with the SNOW package for instance) and would be willing to share a few pointers on how to achieve it. I have a training set of dimensions 905,000 X 5. Should I just get more RAM and run the nnet on one procesor? Or is there a slick way to parallelize the computations? I have tried to

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

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

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 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, I'm using the R nnet package to train a classifier to recognise items which belong to a particular class and those which don't belong to the class. I'm supplying nnet with a matrix x containing training examples (in each row) and a matrix y of targets. The training set is made up of 200 positive examples and 1000 negative examples. I want to train the network on the same

Hello there, I'm back again with another question about the neural network package. I'm having trouble getting the network to run for the maximum number of iterations. It always stops early, usually after 100 iterations claiming to have converged at an answer. Now, for my purposes I want it to run for the entire number of epochs, and I'm been looking at modifying the abstol

I am new to R and use nnetTs - calls. If a time series of let's say 80000 Datapoints and did call nnetTs I want make a new net for the old ponts plus the next 1000 points (81000 datapoints total) what would again cost much calculation time. So I want to pre-init the new net with the former wonnen net to reduce the necessary iteration numbers. Is thee a possibility to do that and how? i.e.:

Dear R users, I have been playing with the nnet and predict.nnet functions and have two questions. 1) Is it possible to specify a validation set as well as a training set in the nnet function before using predict.nnet to test the nnet object against new data? 2) Is it possible to specify more than one layer of neurons? Thanks in advance Matt Oliver

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

Hello everybody, I work in a production plant as an operations analyst. I have been using R for two years, starting with my final dissertation project at college. We have the following problem in our plant. At the end of the production process, each joint (that is what we produce) must pass a final electrical test. The result can be 0 or 1. We think that this may depend on some raw

Dear useRs, I am pleased to announce the release of the new package PNN. PNN implements the algorithm proposed by Specht (1990). It is written in the R statistical language. It solves a common problem in automatic learning. Knowing a set of observations described by a vector of quantitative variables, we classify them in a given number of groups. Then, the algorithm is trained with this datasets

Dear useRs, I am pleased to announce the release of the new package PNN. PNN implements the algorithm proposed by Specht (1990). It is written in the R statistical language. It solves a common problem in automatic learning. Knowing a set of observations described by a vector of quantitative variables, we classify them in a given number of groups. Then, the algorithm is trained with this datasets

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, Please bear with me as I never use NN in R before. I have a network whose my output has, says K node. I would like to put a set of constraints on this layer. Indeed, I have two type of constraints. The first type is that their outputs should sum up to one. The second type is monotonic increasing from the first output node to the K-th node. How can I achieve this? Thank you so much in advance.