search for: svm

If do: > library("e1071") > example(svm) I get: svm> data(iris) svm> attach(iris) svm> ## classification mode svm> # default with factor response: svm> model <- svm(Species ~ ., data = iris) svm> # alternatively the traditional interface: svm> x <- subset(iris, select = -Species) svm> y <- Species...

Hi I am doing this sort of thing: POLY: > > obj = best.tune(svm, similarity ~., data = training, kernel = "polynomial") > summary(obj) Call: best.tune(svm, similarity ~ ., data = training, kernel = "polynomial") Parameters: SVM-Type: eps-regression SVM-Kernel: polynomial cost: 1 degree: 3 gamma: 0.0454...

Dear community, I'm performing svm-regression with svm at library e1071. As I wrote in another post: "svm e1071 call - different results", I get different results if I use the svm.default rather than the svm.formula, being better the ones at svm.formula I've debugged both options. While debugging the svm.formula...

...me significantly different results for very simple data set, with linear kernel significantly outperforming polynomial kernel. *** mse, r2 = 0.5, 0.9 for linear *** mse, r2 = 1.8, 0.1 for polynomial What am I missing ? Ryszard P.S. Here are my results: # simple cross validation function cv.svm <- function(formula, data, ntry = 3, kernel = "linear", scale = FALSE, cross = 3, gamma = 1/(dim(data)-1), degree = 3) { mse <- 0; r2 <- 0 for (n in 1:ntry) { svm.model <- svm(formula , data = data, scale = scale, kernel = kernel,...

...t; This function will be used to test if the descriptor-table registers access is already tracked by another user. Signed-off-by: Nicu?or C??u <ncitu at bitdefender.com> Signed-off-by: Adalbert Laz?r <alazar at bitdefender.com> --- arch/x86/include/asm/kvm_host.h | 1 + arch/x86/kvm/svm/svm.c | 15 +++++++++++++++ arch/x86/kvm/svm/svm.h | 7 +++++++ arch/x86/kvm/vmx/vmx.c | 8 ++++++++ 4 files changed, 31 insertions(+) diff --git a/arch/x86/include/asm/kvm_host.h b/arch/x86/include/asm/kvm_host.h index 83dfa0247130..2ed1e5621ccf 100644 --- a/arch/x86/...

> x <- train[,c( 2:18, 20:21, 24, 27:31)] > y <- train$out > > svm.pr <- svm(x, y, probability = TRUE, method="C-classification", kernel="radial", cost=bestc, gamma=bestg, cross=10) > > pred <- predict(svm.pr, valid[,c( 2:18, 20:21, 24, 27:31)], decision.values = TRUE, probability = TRUE) > attr(pred, "decision.values&q...

Hi, I have a question about the parameter C (cost) in svm function in e1071. I thought larger C is prone to overfitting than smaller C, and hence leads to more support vectors. However, using the Wisconsin breast cancer example on the link: http://planatscher.net/svmtut/svmtut.html I found that the largest cost have fewest support vectors, which is contra...

From: Nicu?or C??u <ncitu at bitdefender.com> This is needed in order to handle clients controlling the MSR related VM-exits. Signed-off-by: Nicu?or C??u <ncitu at bitdefender.com> Signed-off-by: Adalbert Laz?r <alazar at bitdefender.com> --- arch/x86/kvm/svm.c | 27 +++++++++++++++------------ 1 file changed, 15 insertions(+), 12 deletions(-) diff --git a/arch/x86/kvm/svm.c b/arch/x86/kvm/svm.c index 0021d8c2feca..174ced633b60 100644 --- a/arch/x86/kvm/svm.c +++ b/arch/x86/kvm/svm.c @@ -1077,7 +1077,8 @@ static bool msr_write_intercepted(struct kvm_vc...

svm.fit<-svm(as.factor(out) ~ ., data=all_h, method="C-classification", kernel="radial", cost=bestc, gamma=bestg, cross=10) # model fitting svm.pred<-predict(svm.fit, hh, decision.values = TRUE, probability = TRUE) # find the probability, but can not find. attr(svm.pred, &qu...

*Hi, *Does anyone know how can I show an *ROC curve for R-SVM*? I understand in R-SVM we are not optimizing over SVM cost parameter. Any example ROC for R-SVM code or guidance can be really useful. Thanks, Angel. [[alternative HTML version deleted]]

I run the following code, all other is ok, but plot(m.svm,p5.new,As~Cur) is not ok Anyone know why? install.packages("e1071") library(e1071) library(MASS) p5 <- read.csv("http://www.public.iastate.edu/~aiminy/data/p_5_2.csv") p5.new<-subset(p5,select=-Ms) p5.new$Y<-factor(p5.new$Y) levels(p5.new$Y) <- list(Out=c(1), In=c(...

I run the following code, all other is ok, but plot(m.svm,p5.new,As~Cur) is not ok Anyone know why? install.packages("e1071") library(e1071) library(MASS) p5 <- read.csv("http://www.public.iastate.edu/~aiminy/data/p_5_2.csv") p5.new<-subset(p5,select=-Ms) p5.new$Y<-factor(p5.new$Y) levels(p5.new$Y) <- list(Out=c(1), In=c(...

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 r...

Hi, I'm working with support vector machine for the classification purpose, and I have a problem about the accuracy of prediction. I divided my data set in train (1/3 of enteire data set) and test (2/3 of data set) using the "sample" function. Each time I perform the svm model I obtain different result, according with the result of the "sample" function. I would like to "stabilize" the performance of my analysis. To do this I used the "set.seed" function. Is there a better way to do this? Should I perform a bootstrap on my work-flo...

Hi I am trying to fit an svm to predict speech recognition errors. I am using best.svm like this: svm.model = best.svm(data[1:3000,1:23],data[1:3000,24],tunecontrol = tune.control()) I got this: > print(svm.model) Call: best.svm(x = data[1:3000, 1:23], tunecontrol = tune.control(), data[1:3000, 24]) Parameters...

Hello, I'm using the e1071 library for SVM functions. I can quickly train an SVM with: svm(formula = label ~ ., data = testdata) That works well. I want to tune the parameters, so I tried: tune.svm(label ~ ., data=testdata[1:2000, ], gamma=10^(-6:3), cost=10^(1:2)) THIS FAILS WITH AN ERROR: 'names' attribute [199] must be the s...

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 i...

Hi everyone, Can someone please help me in these questions?: 1)if I use crossvalidation with svm, do I have to use this equation to calculate RMSE?: mymodel <- svm(myformula,data=mydata,cross=10) sqrt(mean(mymodel$MSE)) But if I don’t use crossvalidation, I have to use the following to calculate RMSE: mymodel <- svm(myformula,data=mydata) mytest &l...

From: Nicu?or C??u <ncitu at bitdefender.com> This function is needed for the KVMI_EVENT_DESCRIPTOR event. Signed-off-by: Nicu?or C??u <ncitu at bitdefender.com> Signed-off-by: Adalbert Laz?r <alazar at bitdefender.com> --- arch/x86/kvm/svm/svm.c | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/arch/x86/kvm/svm/svm.c b/arch/x86/kvm/svm/svm.c index f4d882ca0060..b540af04b384 100644 --- a/arch/x86/kvm/svm/svm.c +++ b/arch/x86/kvm/svm/svm.c @@ -2220,6 +2220,13 @@ static int rsm_interception(struct vcpu_svm *svm) ret...

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 g...