Quote:
Originally Posted by alasdairj
Well, the problem was to minimize wrt w and b the value w'w (which is a combination of 2 values of our variable) subject to yn(w'xn + b) >= 1 where yn and xn are constants (our training data) i.e. a linear combination of our variable w.
So, the original problem for hardmargin SVM seems like a "quadratic programming" problem  so my real question is this: why do we do the "dual" mapping to get the problem stated in terms of alpha? Is this purely to get it into a more convenient form for QP packages? I am missing something, but I don't know what :).

If I understand correctly, the motivation is that it is often computationally efficient to do so.