Quote:
Originally Posted by butterscotch
Seems good to me. Are you getting the same number of support vectors with Sendai's post? You might want to verify how you calculate the error. The sv_coefficients are not just "alpha", but "y*alpha"

Thanks ButterScotch for pointing this out about the sv coefficients. I have been looking at getting the errors in the test data without relying on the svmpredict function. Once I run the training using svmtrain, I take the resulting model file and extract the support vectors and their coefficients, taking care that the coefficients are "y*alpha".
If my understanding is correct, the support vectors are some points from the input data set (in particular, the points that are "supporting" the decision boundary.)
So I expect that the support vectors that are being reported in the model file should be found in the raw training data. But for some reason, I do not see that. None of the support vectors that the package calculates are in the raw data.
Am I missing something? How would one go about constructing the final hypothesis from the support vectors and coefficients that are reported in the model file?