similar to: R-help(tune.svm)

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

I dont know how to tell to the function 'svm' in this two cases: tune.svm or tune (svm...) the type (I want regression, but by default it works with classification) and the specify kind of kernel (by default it work with radial)... thank you so much!!! -- View this message in context: http://r.789695.n4.nabble.com/How-to-use-tune-svm-or-tune-svm-for-regression-tp4643331.html Sent

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 same length as the vector [184] I don't

Hi, I run the following tuning function for svm. It's very strange that every time i run this function, the best.parameters give different values. [A] >svm.tune <- tune(svm, train.x, train.y, validation.x=train.x, validation.y=train.y, ranges = list(gamma = 2^(-1:2), cost = 2^(-3:2))) # where train.x and train.y are matrix

Hello, I hope you can help me! I`m trying to tune svm parameters: cost and gamma for a landsat image classification, but I get an error and I can't understand it. I write this: > tune(svm, Class~., data = mdt01bis, ranges = list(gamma = 2^(-15:3), cost > = 2^(-5:15))) and R gives: Error en predict.svm(model, if (!is.null(validation.x)) validation.x else if (useFormula)

Dear Sir, I am stuck with a nagging problem in using R for SVM regression. My data has 5 dimensions and 400 observations. The independent variables are : Peb, Ksub, Sub, and Xtt. The dependent variable is: Rexp. I tried using the svm.tune function as well as <_tune(svm.....), to tune the hyper parameters: gamma, epsilon and C. Since I am new to R, I am probably not using the svm.tune

Dear All , 1- I'm trying to access the values of fitted(model) after model<- tune.svm( ) but seemingly it is not poosible. How can I access to values of fitted ? However ,it is possible only after model<- svm( ) 2- How can I access to the other values such as the number of Support Vectors , gamma, cost , nu , epsilon , after model<- tune.svm( ) ? these are not possible? I

Hi, I am trying to use tune.svm in e1071 package. the command i use is tobj <- tune.svm(labels, data= data, cost = 10^(1:2)) Should the last column of the 'data' contain the labels as well? I want to use the linear kernel. But it gives me the error "Error in model.frame.default(formula, data) : 'data' must be a data.frame, not a matrix or an array" Do you know why

I use tune.svm to tune gamma and cost for my training dataset. I use PC, it runs very slowly. Does anyone know how to make it faster? Aimin

Hi, I'm switch over from RapidMiner to R. (The learning curve is steep, but there is so much more I can do with R and it runs much faster overall.) In RapidMiner, I can "tune" a parameter of my svm in a nice cross validation loop. The process will print out the progress as it goes. So for a 5x cross tuning for the value of C with auc as my performance measure, I see XV C

Hi all - (Re my previous question to the list, I managed to generate an ROC plot for my SVM by ranking the data using the Decision.Values property. Thanks for your responses) I have now started tuning the SVM to get optimal parameters for the RBF kernel and I ran into a problem. Whatever parameter ranges I specify, I always get the same error values for all combinations of parameters it

Hello, if I try to tune my svm with the code: Tune <- tune.svm(Data.Train, Class.Train, type="C-classification", kernel="radial", gamma = 2^(-1:1), cost = 2^(2:4)) i get a windows Messagebox with a error in the application "Rgui.exe" and the message: "Die Anweisung in 0x6c48174d verweist auf Speicher 0x00000000. Der Vorgang "read" konnte nicht auf

Hello, Is the first time I am using SNOW package and I am trying to tune the cost parameter for a linear SVM, where the cost (variable cost1) takes 10 values between 0.5 and 30. I have a large dataset and a pc which is not very powerful, so I need to tune the parameters using both CPUs of the pc. Somehow I cannot manage to do it. It seems that both CPUs are fitting the model for the same values

Dear List, With regard to the question I previously raised, here is the result I obtained right now, brglm() does help, but there are two situations: 1) Classifiers with extremely high AIC (over 200), no perfect separation, coefficients converge. in this case, using brglm() does help! It stabilize the AIC, and the classification power is better. Code and output: (need to install package:

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: 0.04545455 coef.0: 0

HI, GUYS, I used the following codes to run SVM and get prediction on new data set hh. dim(all_h) [1] 2034 24 dim(hh) # it contains all the variables besides the variables in all_h data set. [1] 640 415 require(e1071) svm.tune<-tune(svm, as.factor(out) ~ ., data=all_h, ranges=list(gamma=2^(-5:5), cost=2^(-5:5)))# find the best parameters. bestg<-svm.tune$best.parameters[[1]]

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: SVM-Type: eps-regression SVM-Kernel: radial cost: 1

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

Hi I am trying to tune an svm by doing the following: tune(svm, similarity ~., data = training, degree = 2^(1:2), gamma = 2^(-1:1), coef0 = 2^(-1:1), cost = 2^(2:4), type = "polynomial") but I am getting Error in svm.default(x, y, scale = scale, ...) : wrong type specification! > I have to admit I am not sure what I am doing wrong. Could anyone tell me why the

Hello I am playing around trying to bootstrap an svm model using a training set and a test set. I've written another function, auc, which I call here, and am bootstrapping. I did this successfully with logistic regression, but I am getting an error from the starred ** line which I determined with print statements. How do I tune an svm in a bootstrap? I can't find sample code