Machine learning algorithms often do not require the Vector representation of the objects of relevance: often the Inner Product between any two objects suffices. This allows us to efficiently perform calculations on ore useful, albeit larger, Hilbert Spaces by the construction of an appropriate kernel: , where is our Hilbert Space. The kernel often needs to be a valid Inner Product in the higher dimentional Hilbert Space, which can often be ensured via Mercer theorem.