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When we rst started working on the problem of making the kernel machine
approach applicable to the classication of graphs a couple of years ago,
our eorts were mainly driven by the fact that kernel methods had led to
impressive performance results on many data sets. It didn't take us long to
appreciate the sheer elegance of how dicult pattern recognition problems
can be addressed by means of kernel machines, which is particularly the
case for complex data structures such as graphs. To witness researchers
from a large variety of domains bring together new perspectives on kernel
machines and their applications has always been exciting. We are very delighted
that we have been given the opportunity to address a few extremely
interesting open issues in such a rapidly evolving research eld. In this
book, which is an extended and revised version of the rst author's PhD
thesis, we present the major results of our work related to graph kernels.
We give an introduction to the general problem, discuss theoretical issues
of graph matching and kernel machines, present a number of error-tolerant
graph kernels applicable to a wide variety of dierent graphs, and give an
experimental evaluation on real-world data. |
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