similar to: how to interpret coefficients from multiclass svm using libsvm (for multiclass R-SVM)

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

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, 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

I used the c binary svm-train in LIBSVM to create a model file. How do I load this model file into R? Is there a function in R package e1071 that accomplishes this operation? Thanks, Yin -- View this message in context: http://r.789695.n4.nabble.com/Loading-externally-created-LIBSVM-model-into-R-tp4407853p4407853.html Sent from the R devel mailing list archive at Nabble.com.

> > So i guess from your prev. email the svmModel$coefs correspond to the > "Alpha" . yes (times the sign of y!). > > Why do I see three columns in the coefs?( Is this the number of classes -1 > = Numbe of hyperplanes) yes, but in a packed format which is not trivial. I attach some explanation I sent to R-help some time ago (the guy wanted to write his own

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

Hi list, I'm toying with the idea of writing a m680x0 backend for LLVM, and the address modes of this chip are bewildering, to say the least. Here's a rough list off wikipedia for reference: * Register direct o data register, e.g. "D0" o address register, e.g. "A6" * Register indirect o Simple address, e.g. (A0) o

On Tue, Feb 10, 2009 at 8:27 AM, Villmow, Micah <Micah.Villmow at amd.com> wrote: > Bill, > Sorry if I wasn't clear enough. I wasn't referring to multiclass's that > define other classes, but with using patterns inside of a multiclass to > reduce redundant code. > For example: > multiclass IntSubtract<SDNode node> > { > def _i8 : Pat<(sub

Bill, Sorry if I wasn't clear enough. I wasn't referring to multiclass's that define other classes, but with using patterns inside of a multiclass to reduce redundant code. For example: multiclass IntSubtract<SDNode node> { def _i8 : Pat<(sub GPRI8:$src0, GPRI8:$src1), (ADD_i8 GPRI8:$src0, (NEGATE_i8 GPRI8:$src1))>; def _i32 : Pat<(sub

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 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

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 all, I'm using the samr-package to identify significantly differentially expressed genes in microarray data. So far, I had no problems, but when I used a large multiclass data set with 327 samples, I obtained the following error/warning message: Warning message: Inf In factorial(length(y)) : value out of range in 'gammafn' Since y is the label vector and has length 327, the

On 08/22/2017 03:59 AM, Alex Bradbury via llvm-dev wrote: > On 21 August 2017 at 13:23, Martin J. O'Riordan via llvm-dev > <llvm-dev at lists.llvm.org> wrote: >> But there is a downside. >> >> For each of the above I also have variations that are a result of different >> processor and ISA versions, and because of this I have to use >> ‘multiclass/defm’

I have been reading the “RFC/bikeshedding: Separation of instruction and pattern definitions in LLVM backends” topic with considerable interest. This is an approach I have been considering for taming our own large instruction set, and it looks like it structures our descriptions better than the conventional approach we have used so far. However, I have another form of TableGen taming that I

In TableGen, can multiclasses inherit from one another? I notice that there's a lot of redundancy in the X86 .td files that could go away with multiclass inheritance. -Dave

On Mar 23, 2009, at 5:14 PM, David Greene wrote: > In TableGen, can multiclasses inherit from one another? I notice > that there's > a lot of redundancy in the X86 .td files that could go away with > multiclass > inheritance. Nope, not currently. That would be a nice feature though! -Chris

Hello all, I'm trying to run a gird parameter search for a svm. Therefore I'M using the ksvm function from the kernlab package. ---- svp <- ksvm(Ktrain,ytrain,type="nu-svc",nu=C) ---- The problem is that the optimization algorithm does not return for certain parameters. I tried to use setTimeLimit() but that doesn't seem to help. I suspect that ksvm() calls c code that

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