similar to: TR: [e1071] Load an SVM model exported with write.svm

Hello, I have an SVM model previously calibrated using libsvm R implementation from the e1071 package. I would like to use this SVM to predict values, from a Java program. I first tried to use jlibsvm and the "standard" java implementation of libsvm, without success. Thus, I am now considering writing data in files from my Java code, calling an R program to predict values, then gather

Dear David, Dear Friends, After any running svm I receive different results of Error estimation of 'svm' using 10-fold cross validation. What is the reason ? It is caused by the algorithm, libsvm , e1071 or something els? Which value can be optimal one ? How much run can reach to the optimality.And finally, what is difference between Error estimation of svm using 10-fold cross validation

Hello List, I am having a great trouble using svm function in e1071 package. I have 4gb of data that i want to use to train svm. I am using Amazon cloud, my Amazon Machine Image(AMI) has 34.2 GB of memory. my R process was killed several times when i tried to use 4GB of data for svm. Now I am using a subset of that data and it is only 1.4 GB. i remove all unnecessary objects before calling

I am starting to use svm from e1071 and I wonder how exactly crossvalidation is implemented. Whenever I run > svm.model <- svm(y ~ ., data = trainset, cross = 3) on my data I get dirrerent values for svm.model$MSE e.g. [1] 0.9517001 1.7069627 0.6108726 [1] 0.3634670 0.9165497 1.4606322 This suggests to me that data are scrambled each time - the last time I looked at libsvm python

Full_Name: Julien Gagneur Version: 2.2.1 OS: Linux (Suse 9.3) Submission from: (NULL) (194.94.44.4) Using the SparseM library (SparseM_0.66) and the e1071 library (e1071_1.5-12) I fail using svm method with a sparse matrix. Here is a sample example. I experienced the same problem under Windows. > library(SparseM) [1] "SparseM library loaded" > library("e1071")

Hello, When calling several times the svm() function, I get different results. Do I miss something, or is there some random generation in the C library? In this second hypothesis, is it possible to fix an eventual seed? Thank you Pierre ### Example library('e1071') x = rnorm(100) # train set y = rnorm(100) c = runif(100)>0.5 x2 = rnorm(100)# test set y2 = rnorm(100) # learning a

The following code gives me an error: class_vars = [''fruit'',''cow'',''coffee''].each do |class_var| "#{class_var}".classify.send(:find, 1 + rand(10)) end The error message stack: LocalJumpError: no block given from (irb):34:in `find'' from (irb):33:in `each'' from (irb):33:in `find''

Hello, I have been searching a way to get the resulting optimized function value of a trained SVM model (svm from the package e1071) but I have not succeed. Does anyone knows a way to get that value? Pau

I am accustomed to the LIBSVM package, which provides cross-validation on training with the -v option % svm-train -v 5 ... This does 5 fold cross validation while building the model and avoids over-fitting. But I don't see how to accomplish that in the e1071 package. (I learned that svm(... cross=5 ...) only _tests_ using cross-validation -- it doesn't affect the training.) Can

I am using SVM to classify categorical data and I would like the probabilities instead of the classification. ?predict.svm says that its only enabled when you train the model with it enabled, so I did that, but it didn't work. I can't even get it to work with iris. The help file shows that probability = TRUE when training the model, but doesn't show an example. Then I try to

Dear David, Dear R Users , Suppose that we want to regress for example a certain autoregressive model using SVM. We have our data and also some fixed kernels in libSVM behinde e1071 in front. The question: Where can we insert our certain autoregressive model ? During creating data frame ? Or perhaps we can make a relationship between our variables ended to desired autoregressive model ?

I'm not sure what the parameter specifies: random if an integer value is specified, random parameter vectors are drawn from the parameter space. What are the parameter vectors and what is the parameter space? What means drawn? greetings Jessi [[alternative HTML version deleted]]

Dear all, I am trying to compile the package e1071 (version 1.3-11) with R CMD INSTALL. I tried with R 1.7.0 on Redhat Linux 2.4.7-10 and R 1.6.2 on Linux 2.4.9-34smp but keep getting the same error message during configure : WARNING: g++ 2.96 cannot reliably be used with this package. Please use a different compiler. Can anyone help me with this or at least point me in the right direction ?

hi, i am really sorry to ask this on the list, but i havent been able to find anything on this topic. i would like to know how the various parameters in the svm function call in library e1071 work. all the literature that i was able to find on the internet have been on the mathematics and derivation of equations of the SVM or some very specific examples that relate to biostatistics. i have been

Hi All, In package e1071 for SVM based classification, one can get a probability measure for each prediction. I like to know what is method that is used for calculating this probability. Is it calculated using logistic link function? Thanks for your help. Regards, Raj

Hallo, I have a question concerning SVM regression in R. I intend to use SVMs for feature selection (and knowledge discovery). For this purpose I will need to extract the weights that are associated with my features. I understand from a previous thread on SVM classification, that predictive models can be derived from SVs, coefficiants and rhos, but it is unclear for me how to transfer this

Hello, As far I saw in archive mailing list, I am not the first person with this problem. Anyway I was not able to pass this error once the information I got from the archive it is not very conclusive for this case. I have used linear, radial and sigmoid kernels for the same data in the same conditions and everything is ok. This problem just happens with the polynomial kernel. I send the

Dear R Community- I am a new user of support vector machines for species distribution modeling and am using package e1071 to run svm() and predict.svm(). Briefly, I want to create an svm model for classification of a factor response (species presence or absence) based on climate predictor variables. I have used a training dataset to train the model, and tested it against a validation data set

hii all!!! could anyone tell me how to use libsvm in R.. i am not able to find good way to use it.... -- View this message in context: http://r.789695.n4.nabble.com/about-libsvm-tp3007214p3007214.html Sent from the R help mailing list archive at Nabble.com.

Hello all, I'm working with the svm (libsvm) implementation from library(e1071). Currently I'm trying to extend recursive feature elimination (R-SMV) to work with multiclass classification. My problem is that if I run svm for a 3 class problem I get a 2-D vector back from model$coefs, can someone explain me what this values are? I understand them in the 2-class problem where this is a