The individual kernels are normalized-this plays an important role in allowing us to integrate information from heterogeneous feature spaces.
for all where φ is an (implicit) mapping from X to an (inner product) feature space F. Kernels are used inside learning algorithms such as support vector machines (SVM) or kernel perceptrons as the interface between the algorithm and the data.