similar to: svm of e1071 package

Dear List, I am trying to read some files using read.csv and total size of those files are 3.99 GB. I am using MacBook Pro with 4GB RAM(snow leopard). I also tried to run a chunk from those files and altogether the size was 1.33 GB. But every time I was getting the following error R(1200) malloc: *** mmap(size=16777216) failed (error code=12) *** error: can't allocate region *** set a

Thibault, It would be nice if LiblineaR() accepted data in the form of a sparse matrix (it does not accept whatever e1071::read.matrix.csr returns). It would also be nice if there were functions to read/write files in the native liblinear file format; I am sure the original liblinear library provides at least the input code. Thanks! -- Sam Steingold (http://sds.podval.org/) on Ubuntu 12.04

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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

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")

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, 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

How do I read/write liblinear models to files? E.g., if I train a model using the command line interface, I might want to load it into R to look the histogram of the weights. Or I might want to train a model in R and then apply it using a command line interface. -- Sam Steingold (http://sds.podval.org/) on Ubuntu 12.04 (precise) X 11.0.11103000 http://www.childpsy.net/

Hello, In my ongoing quest to develop a "best" model, I'm testing various forms of SVM to see which is best for my application. I have been using the SVM from the e1071 library without problem for several weeks. Now, I'm interested in RVM and LSSVM to see if I get better performance. When running RVM or LSSVM on the exact same data as the SVM{e1071}, I get an error that I

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 all, I am currently looking for a book about support vector machines for regression and classification and am a bit lost since they are plenty of books dealing with this subject. I am not totally new to the field and would like to get more information on that subject for later use with the e1071 <http://cran.r-project.org/web/packages/e1071/index.html> package for instance. Does

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, I have a process using svm from the e1071 library. it works. I want to try using the KSVM library instead. The same data used wiht e1071 gives me an error with KSVM. My data is a data.frame. sample code: svm_formula <- formula(y ~ a + B + C) svm_model <- ksvm(formula, data=train_data, type="C-svc", kernel="rbfdot", C=1) I get the following error:

Hola a todos, estoy intentado entrenar un modelo SVM y me da con el valor de nu=0.1 el siguiente error Error en predict.svm(ret, xhold) : NA/NaN/Inf en llamada a una función externa (arg 8) Según varío el valor de nu a valores menores me da el mismo cuelgue. En cambio si uso valores mayores de nu el ordenador simplemente se cuelga ¿tendrá esto remedio? Uso la librería e1071 ¿existe alguna

Hi, My question might be a little general. I have a number of values to select for the complexity parameters in some classifier, e.g. the C and gamma in SVM with RBF kernel. The selection is based on which values give the smallest cross validation error. I wonder if the randomized splitting of the available dataset into folds is done only once for all those choices for the parameter values, or

Dear all, I am exploring the possibilities for automated classification of my data. I have successfully used KNN, but was thinking about looking at SVM (which I did nto use before). I have a pairwise distance matrix of training observations which are classified in set classes, and a distance matrix of new observations to the training ones. Is it possible to use distance matrices for SVM, and

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 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 ?

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