similar to: Are any R packages available for Multi class Multi Label SVM?

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(0)) attach(p5.new)

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(0)) attach(p5.new)

Hi, If I try to read the codes of functions in e1071 package, it gives me following error message. >library(e1071) > svm function (x, ...) UseMethod("svm") <environment: namespace:e1071> > predict.svm Error: Object "predict.svm" not found > Can someone help me on this how to read the codes of the functions in the e1071 package? Thanks. Raj

1. This is _not_ a bug in R itself. Please don't use R's bug reporting system for contributed packages. 2. This is _not_ a bug in svm() in `e1071'. I believe you forgot to take sqrt. 3. You really should use the `tot.MSE' component rather than the mean of the `MSE' component, but this is only a very small difference. So, instead of spread[i] <- mean(mysvm$MSE), you

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

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

Full_Name: Noel O'Boyle Version: 2.1.0 OS: Debian GNU/Linux Sarge Submission from: (NULL) (131.111.8.96) (1) Description of error The 10-fold CV option for the svm function in e1071 appears to give incorrect results for the rmse. The example code in (3) uses the example regression data in the svm documentation. The rmse for internal prediction is 0.24. It is expected the 10-fold CV rmse

Hello all, I am using the e1071 SVM with the tune options for classification, which work pretty well, given the examples of using tune.svm function for classification. But I have not found any example to tune the SVM novelty detection (one-classification) parameters (gamma, cost, nu), for example this are some of the options I have tried with no success: obj<-tune(svm, x,y, type

Hi, I ran two svm models in R e1071 package: the first without cross-validation and the second with 10-fold cross-validation. I used the following syntax: #Model 1: Without cross-validation: > svm.model <- svm(Response ~ ., data=data.df, type="C-classification", > kernel="linear", cost=1) > predict <- fitted(svm.model) > cm <- table(predict,

Hi, I ran two svm models in R e1071 package: the first without cross-validation and the second with 10-fold cross-validation. I used the following syntax: #Model 1: Without cross-validation: > svm.model <- svm(Response ~ ., data=data.df, type="C-classification", > kernel="linear", cost=1) > predict <- fitted(svm.model) > cm <- table(predict,

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

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, I've seen that when I reach the call: ret <-

> install.packages("e1071") Warning: package 'e1071' is in use and will not be installed > library(e1071) > library(MASS) > p5 <- read.csv("http://www.public.iastate.edu/~aiminy/data/p_5_2.csv") > attach(p5) The following object(s) are masked from p5 ( position 3 ) : Aa As Cur Ms P Y The following object(s) are

Dear all, I got a code for e1071 package in R for SVM regression. I have used *m$coefs* for extracting the coefficients but I am getting only 72 . How can I extract coefficients of the predictors set? Does it mean that I will get only 72 as *Number of Support Vectors: 72. * ** Thanks in advance Code: -------------- library(e1071) > # create data > x <- seq(0.1, 5,

Hi. I'm trying to plot a pair of intertwined spirals and an svm that separates them. I'm having some trouble. Here's what I tried. > library(mlbench) > library(e1071) Loading required package: class > raw <- mlbench.spirals(200,2) > spiral <- data.frame(class=as.factor(raw$classes), x=raw$x[,1], y=raw$x[,2]) > m <- svm(class~., data=spiral) > plot(m,

Dear All: Just fixed where is the problem I am trying to use the R function "svm" with "type~." , but I got the following error message SVM.Model1 <- svm(type ~ ., data=my.data.x1x2y, *type='C-classification'*, kernel='linear',scale=FALSE) *Error in eval(predvars, data, env) : object 'type' not found* I am wondering if I should install a

Hi R, I need to see the inner code behind the function "svm" in the package e1071. I enter svm in the console and get the below output. > svm function (x, ...) UseMethod("svm") <environment: namespace:e1071> Is there any way I can look into the code of what svm (support vector machine) is doing? Thanks a lot for your help... Thanks and Regards,

Dear All: I am trying to use the R function "svm" with "type =C-classification" , but I got the following error message SVM.Model1 <- svm(type ~ ., data=my.data.x1x2y, *type='C-classification'*, kernel='linear',scale=FALSE) *Error in eval(predvars, data, env) : object 'type' not found* I am wondering if I should install a specific R

Dear all, I executed svm calculation using e1071 library with a microarray data (http://www.iu.a.u-tokyo.ac.jp/~kadota/R/data_Singh_RMA_3274.txt). Then, I shuffled the data samples and executed svm calculation again. The results of 2 calculation were different (in SV, coefs and weights). I attached the script below. Could please tell me why this happens? If possible please tell me how to make

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