similar to: Loading externally created LIBSVM model into R.

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

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

Dear R users, There is a error message when I run the following code. It is used to load microarray data and use the top 1000 genes for training svm to classify test set . > library(e1071) Loading required package: class > f=read.table("F:\\lab\\ microarray analysis\\VEH LPS\\exprs.txt",

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

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

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.

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

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

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

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

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 ?

Hi-- While I agree that we cannot agree on the ideal algorithms, we should be taking practical steps to implement microarrays in the clinic. I think we can all agree that our algorithms have some degree of efficacy over and above conventional diagnostic techniques. If patients are dying from lack of diagnostic accuracy, I think we have to work hard to use this technology to help them, if we

Hi, the new version 1.1-0 of package e1071 is now on CRAN. Changes: *) use libsvm 2.1 for support vector machines. We are now also a kind of ``official'' R frontend to libsvm and linked from their homepage at http://www.csie.ntu.edu.tw/~cjlin/libsvm *) new functions for comparing partitions Best, Fritz -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-

Hello all, I am trying to use svm (from the e1071 package) to solve a binary classification problem. The two classes in my particular data set are unequally populated. class 'I' (for important) has about 3000 instances while class "B" (for background) has about 20,000. experimenting with different classifiers I realized that in cases where such an asymmetry exists there is a

A new version of e1071 has been released to CRAN which should be much easier to install on a lot of platforms because reading/writing PNM images has been moved to the pixmap package, hence there are no longer dependencies on external libraries and no configure mechanism. For the authors, Fritz Leisch ********************************************************** Changes in Version 1.2-0: o

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 all, this is the first time I'm using R and e1071 package and SVM multiclass (and I'm not a statistician)! I'm very confused, then. The goal is: I have a sentence with sunny; it will be classified as "yes" sentence; I have a sentence with cloud, it will be classified as "maybe"; I have a sentence with rainy il will be classified as "no". The

Hello, all experts, My major is computer-aied drug design ( main QSAR). Now, my paper need be reviesed, and one reviewer ask me do genetic algorithm coupled with gaussian process method (GA+GP). my data: training set: 191*106 test set: 73*106 here, I need use GA+GP to do variable selection when building the model. In R, there are not GA package like in matlab

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

Hi R users, I'm trying to run a SVM - regression using e1071 package but the function svm() all the time apply a classification method rather than a regression. svm.m1 <- svm(st ~ ., data = train, cost = 1000, gamma = 1e-03) Parameters: SVM-Type: C-classification SVM-Kernel: radial cost: 1000 gamma: 0.001 Number of Support Vectors: 209