Abstract:
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Kernel-based learning methods are primarily used with real-valued
data. Yet many domains are made up of structured objects such as
strings, trees or graphs. This work focuses on the design of
kernels capable of coping with structured objects. It briefly
introduces kernel-based learning methods and kernel theory, and
goes on to study the basic mechanisms for kernel combination and
the family of convolution kernels, which is meant as the main
building block for a theory of kernels on structured domains.
Additionally, some practical design strategies are identified
through applications of the theory. Finally, some proposals are
outlined aimed at future research. |