[R] one-class SVM in kernlab

[Roberto Perdisci]

Hello,
  I'm trying to using ksvm() in the kernlab package to fit a one-class
SVC, but I get a strage result on the cross-validation error estimate.
For example, consider this code:

data(spam)
classifier <- ksvm(type~.,data=spam[which(spam[,'type']=='spam'),],
type="one-svc",kernel="rbfdot",kpar=list(sigma=0.1),nu=0.05,cross=10)

what I get is:

> classifier
Support Vector Machine object of class "ksvm"

SV type: one-svc  (novelty detection)
 parameter : nu = 0.05

Gaussian Radial Basis kernel function.
 Hyperparameter : sigma =  0.1

Number of Support Vectors : 660

Objective Function Value : 10.5781
Training error : 0.212907
Cross validation error : 0

---

What surprises me is that the Training error (which I suppose is the
resubstitution error) is higher than the cross-validation error. Also,
even changing the value of sigma, the cross-validation error does not
change.
I get similar results with other datasets, too. For example

classifier <- ksvm(Species~.,data=iris[which(iris[,'Species']=='setosa'),],
type="one-svc",kernel="rbfdot",kpar=list(sigma=0.5),nu=0.05,cross=10)


Am I using ksvm() correctly?
I also tried to use the formula ~. exluding the attribute 'type' from
the dataset, and the results are exactly the same.

Unfortunately reading ?ksvm didn't help me much.

thank you,
regards,
Roberto