SVM linear kernel and SV
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 me why ?
In how far does the plot give you the impression is wouldn't? The two classes look pretty separated to me.
In addition, when I change the option 'scale' (from TRUE to FALSE) the results change.
(Which results?) The plot is, of course, slightly different since the model is based on different data, but the class predictions (on the training data) are the same. Why does this surprise you? Could you explain me why ? the option 'scale' of svm()
acts on the dataset or on the weight vector w and threshold b ?
On the data set, and therefore also on w and b. Best, David
Dr. David Meyer Department of Information Systems and Process Management Vienna University of Economics and Business Administration Augasse 2-6, A-1090 Wien, Austria, Europe Fax: +43-1-313 36x746 Tel: +43-1-313 36x4393 HP: http://wi.wu-wien.ac.at/~meyer/