Abstrakt:
Support Vector Machines (SVM’s) with various kernels have become
very successful in pattern classification and regression. However, single kernels
do not lead to optimal data models. Replacing the input space by a kernel-based
feature space in which the linear discrimination problem with margin maximization
is solved is a general method that allows for mixing various kernels and
adding new types of features. We show here how to generate locally optimized
kernels that facilitate multi-resolution and can handle complex data distributions
using simpler models than the standard data formulation may provide.
