ABSTRACT
The kernel approach offers a very general framework for performing pattern analysis on many types of data and it can be used in a wide variety of tasks and application areas. The kernel technique also enables us to use feature spaces whose dimensionality is more than polynomial in the relevant parameters of the systems even though the computational cost of the pattern analysis algorithm remains polynomial.
