Hi,

I have a question about the parameter C (cost) in svm function in e1071. I
thought larger C is prone to overfitting than smaller C, and hence leads to
more support vectors. However, using the Wisconsin breast cancer example on
the link:
http://planatscher.net/svmtut/svmtut.html
I found that the largest cost have fewest support vectors, which is contrary
to what I think. please see the scripts below:
Am I misunderstanding something here?

Thanks a bunch,

-Jack

> model1 <- svm(databctrain, classesbctrain, kernel = "linear", cost = 0.01)
> model2 <- svm(databctrain, classesbctrain, kernel = "linear", cost = 1)
> model3 <- svm(databctrain, classesbctrain, kernel = "linear", cost = 100)
> model1

Call:
svm.default(x = databctrain, y = classesbctrain, kernel = "linear",
    cost = 0.01)


Parameters:
   SVM-Type:  C-classification
 SVM-Kernel:  linear
       cost:  0.01
      gamma:  0.1111111

Number of Support Vectors:  99

> model2

Call:
svm.default(x = databctrain, y = classesbctrain, kernel = "linear",
    cost = 1)


Parameters:
   SVM-Type:  C-classification
 SVM-Kernel:  linear
       cost:  1
      gamma:  0.1111111

Number of Support Vectors:  46

> model3

Call:
svm.default(x = databctrain, y = classesbctrain, kernel = "linear",
    cost = 100)


Parameters:
   SVM-Type:  C-classification
 SVM-Kernel:  linear
       cost:  100
      gamma:  0.1111111

Number of Support Vectors:  44

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