Learning systems based on kernels are a powerful class of algorithms
that includes Support Vector Machines and Gaussian Processes.
These systems have become a major part of current research into and
applications of adaptive systems. Despite this fact very little is known
about when we can expect these systems to perform well. There has even
been the assumption made that they provide a universal learning
methodology. The proposed project will address this in
order to:
provide theoretical tools that describe when a set of
functions can be realised by hyperplanes with non-trivial margins
in some feature space;
describe how the degree of matching
between a kernel and a problem
domain can be measured;
develop methods for choosing kernels as attuned as possible to a
particular problem/domain;
develop alternative `luckiness' functions that give rise to
efficient generic learning methods for problems that cannot be
solved using kernel methods.
Primary investigator
jst
Secondary investigator
aa
Associated research group
Information: Signals, Images, Systems Research Group