I have been thinking of this case as i was reading other questions and answers about SVM.
A question was raised about an SVM model having 1000 data points and 800 support vectors. The OP used a linear kernel. However, this is something that confuses me. If an SVM is supposed to maximise the margin between the 2 classes. How would the increase in number of support vectors feature in this calculations?
How to ensure that all the vectors are maximimum margin from each other?