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.