# How to find max margin for non-separable SVM?

I am new to Machine Learning. Suppose a training set of positive (square) and negative (circle) points is given like:

Obviously there would be no nice linear separator of positive and negative points. So assume there exist a transformation for (x1,x2) points as: y1 = (x1-5)^2, y2 = (x2-5)^2 in (y1,y2) space. We will then map all positive points to (25,25) and all negative points to (1,1).

Now, the question is how can I find the maximum-margin separator in new space? That is, what curve in the original space transforms to the straight-line boundary in the transformed space? Using that I need to determine that should a new point be considered positive or negative.