Hi  
 
I am doing this  sort of thing:
 
POLY:
 
> > obj = best.tune(svm, similarity ~., data = training, kernel =
"polynomial")
> summary(obj)
 
Call:
 best.tune(svm, similarity ~ ., data = training, kernel = "polynomial") 
 
Parameters:
   SVM-Type:  eps-regression 
 SVM-Kernel:  polynomial 
       cost:  1 
     degree:  3 
      gamma:  0.04545455 
     coef.0:  0 
    epsilon:  0.1 
 
 
Number of Support Vectors:  754
 
> svm.model <- svm(similarity ~., data = training, kernel  =
"polynomial", cost = 1, degree = 3, gamma = 0.04545455, coef.0 = 0,
epsilon = 0.1)
> pred=predict(svm.model, testing)
> pred[pred > .5] = 1
> pred[pred <= .5] = 0  
> table(testing$similarity, pred)
   pred
    0  1 
  0 30  8
  1 70 63
> obj = best.tune(svm, similarity ~., data = training, kernel =
"linear")
> summary(obj)
 
LINEAR:
 
Call:
 best.tune(svm, similarity ~ ., data = training, kernel = "linear") 
 
Parameters:
   SVM-Type:  eps-regression 
 SVM-Kernel:  linear 
       cost:  1 
      gamma:  0.04545455 
    epsilon:  0.1 
 
 
Number of Support Vectors:  697
 
> svm.model <- svm(similarity ~., data = training, kernel  = "linear",
cost = 1, gamma = 0.04545455, epsilon = 0.1)
> pred=predict(svm.model, testing)
> pred[pred > .5] = 1
> pred[pred <= .5] = 0  
> table(testing$similarity, pred)
   pred
    0   1  
  0   6  32
  1   4 129
 
 
RADIAL:
 
> obj = best.tune(svm, similarity ~., data = training, kernel =
"radial")
> summary(obj)
 
Call:
 best.tune(svm, similarity ~ ., data = training, kernel = "linear") 
 
Parameters:
   SVM-Type:  eps-regression 
 SVM-Kernel:  linear 
       cost:  1 
      gamma:  0.04545455 
    epsilon:  0.1 
 
 
Number of Support Vectors:  697
 
> svm.model <- svm(similarity ~., data = training, kernel  = "radial",
cost = 1, gamma = 0.04545455, epsilon = 0.1)
> pred=predict(svm.model, testing)
> pred[pred > .5] = 1
> pred[pred <= .5] = 0  
> table(testing$similarity, pred)
   pred
    0  1 
  0 27 11
  1 64 69
 
 
SIGMOID:
 
> obj = best.tune(svm, similarity ~., data = training, kernel =
"sigmoid")
> summary(obj)
 
Call:
 best.tune(svm, similarity ~ ., data = training, kernel = "sigmoid") 
 
Parameters:
   SVM-Type:  eps-regression 
 SVM-Kernel:  sigmoid 
       cost:  1 
      gamma:  0.04545455 
     coef.0:  0 
    epsilon:  0.1 
 
 
Number of Support Vectors:  986
 
> svm.model <- svm(similarity ~., data = training, kernel  = "sigmoid",
cost = 1, gamma = 0.04545455, coef.0 = 0, epsilon = 0.1)
> pred=predict(svm.model, testing)
> pred[pred > .5] = 1
> pred[pred <= .5] = 0  
> table(testing$similarity, pred)
   pred
    0   1  
  0   8  30
  1  26 107
>
 
and then taking out the kappa statistic to see if I am getting anything
significant.
 
I get kappas of 15 - 17% - I don't think that is very good.  I know
kappa is really for comparing the outcomes of two taggers but it seems a
good way to measure if your results might be by chance.
 
Two questions:
 
Any comments on Kappa and what it might be telling me?
 
What can I do to tune my kernels further?
 
Stephen

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