similar to: training svm

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

Hi Sadaf, Out of curiosity, what sorts of things have you tried to fix this? For example, after playing around with this a bit, if I remove your "eps" parameter from your `ranges` list, it works. Perhaps you should try tweaking the values you pick for your parameters. You don't even have to put it in the `tune` function to get an idea of the ranges you should use: R>

What should I do if I need to train svm() with data having same value across all rows in some columns. These must be the important features of the class and we cant exclude these columns to build up models. The error I am getting is: Error in predict.svm(ret, xhold) : Model is empty! In addition: Warning message: In svm.default(datatrain, classtrain) : Variable(s) 'F112' and

Dear Sir/Madam, 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 to tune the hyper parameters: gamma, epsilon and C. I am getting the following error message: Error in predict.svm(ret, xhold,

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

What should I do if I need to train svm() with data having same value in number of columns. These must be the deterministic features for the class. Is there anyway to over come this trouble? Regards --------------------------------- [[alternative HTML version deleted]]

Hello, As far I saw in archive mailing list, I am not the first person with this problem. Anyway I was not able to pass this error once the information I got from the archive it is not very conclusive for this case. I have used linear, radial and sigmoid kernels for the same data in the same conditions and everything is ok. This problem just happens with the polynomial kernel. I send the