Thank you for your answer,
but my problem concerns the support vectors. Indeed the two classes
are well separated and the hyperplane is linear but the support
vectors aren't aligned in parallel to the hyperplane. And according to
me, the support vectors (for each class) should be aligned
Dear all,
I'm a trainee statistician in a company and we'd like to understand svm
mechanism, at first with simple examples.
I use e1071 package and I have several questions. I'm working with data
extracted from cats data (from R). My dataset corresponds to a completely
separable case with a
Gladys,
I've used svm() with a linear kernel and I'd like to plot the linear
hyperplane and the support vectors. I use plot.svm() and, according to
me, I would have found aligned support vectors (because the hyperplane
is linear) for each class but it wasn't the case. Could you explain
