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Hi, Could you please tell me whether SVM can do multiple regression or not? Cheers, Amy _________________________________________________________________ Browse profiles for FREE! Meet local singles online. [[alternative HTML version deleted]]

Hi Could you please explain the line that I got from the documentation of R? does it mean that there is a difference between using and not using the formula interface with SVM ?: If the predictor variables include factors, the formula interface must be used to get a correct model matrix. Cheers, Amy _________________________________________________________________ View photos of

Hi, I understand from help pages that in order to use a data set with svm, I have to divide it into two files: one for the dataset without the class label and the other file contains the class label as the following code:- library(e1071) x<- read.delim("mydataset_except-class-label.txt") y<- read.delim("mydataset_class-labell.txt") model <- svm(x, y, cross=5)

Hi everybody, I am using the contour function to draw isobares. And i would like to to bold contours for an interval of 500m and weak contour for an interval of 100m. Can someone help me with this? Thanks a lot Karine _________________________________________________________________ [[elided Hotmail spam]] [[alternative HTML version deleted]]

hi, could you please tell me what kind of cross validation that SVM of e1071 uses? Cheers, Amy _________________________________________________________________ View photos of singles in your area! Looking for a hot date? [[alternative HTML version deleted]]

Hi, Could you please tell me whether there is a difference in the result of svm when using formula instead of class label? Cheers, Amy _________________________________________________________________ Browse profiles for FREE! Meet local singles online. [[alternative HTML version deleted]]

Hi, Could you please tell me how I correct the following error message? “Error in object$tables[[v]] : subscript out of bounds” This is the code: library(e1071) data(iris) attach(iris) class_label <- names(iris)[1] myformula <- formula(paste(class_label,"~ .")) mymodel<-naiveBayes(myformula, iris,cross=3) predict(mymodel,iris) ##Error in object$tables[[v]] :