What is the difference between the normal soft margin SVM and SVM with a linear kernel ML?


You have the basic SVM – hard margin. This assumes that data is very well behaved, and you can find a perfect classifier – which will have 0 error on train data.


Data is usually not well behaved, so SVM hard margins may not have a solution at all. So we allow for a little bit of error on some points. So the training error will not be 0, but average error over all points is minimized.


The above assume that the best classifier is a straight line. But what is it is not a straight line. (e.g. it is a circle, inside a circle is one class, outside is another class). If we are able to map the data into higher dimensions – the higher dimension may give us a straight line.