Dealing with the test data
If we choose a mapping to a high-D space for
which the kernel trick works, we do not have to
pay a computational price for the high-
dimensionality when we find the best hyper-plane.
We cannot express the hyperplane by using its normal
vector in the high-dimensional space because this
vector would have a huge number of components.
Luckily, we can express it in terms of the support
vectors.
But what about the test data. We cannot compute
the scalar product because its in the
high-D space.