R help - May 2011 - how to train ksvm with spectral kernel (kernlab) in caret?

Hello all,

I would like to use the train function from the caret package to
train a svm with a spectral kernel from the kernlab package. Sadly
a svm with spectral kernel is not among the many methods in caret...

using caret to train svmRadial:
------------------
library(caret)
library(kernlab)

data(iris)
TrainData<- iris[,1:4]
TrainClasses<- iris[,5]

set.seed(2)
fitControl$summaryFunction<- Rand
svmNew<- train(TrainData, TrainClasses,
		method = "svmRadial",
		preProcess = c("center", "scale"),
		metric = "cRand",
		tuneLength = 4)

svmNew
-------------------


here is an example on how to train the
ksvm with spectral kernel
-------------------
# Load the data
data(reuters)
y <- rlabels
x <- reuters

sk <- stringdot(type="spectrum", length=4, normalized=TRUE)
svp <- ksvm(x,y,kernel=sk,scale=c(),cross=5)
svp
-----------------


Does anyone know how I can train the svm from above with using the caret 
package?

best regards

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