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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