Positive definite kernels and their generalizations, as, e.g., the conditionally
positive definite kernels, play an important role in various areas of
mathematics. They are used in scattered data approximation as main ingredients
of radial basis function methods, serve as models for variograms in
statistical methods for spatial data and give rise to the famous kernel trick
used in many methods of machine learning. A reason for the success of
kernel-based methods lies in the intimate connection of positive definite
kernels with reproducing kernel Hilbert spaces. While this is a well-studied
connection, the corresponding relation of conditionally positive definite
kernels and Pontryagin spaces attracted less attention. In this thesis, we want to shed some light on this connection. Furthermore, we extend the notion of conditionally positive definite functions to discrete hypergroups and give some applications to kernel design on graphs.
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Positive definite kernels and their generalizations, as, e.g., the conditionally
positive definite kernels, play an important role in various areas of
mathematics. They are used in scattered data approximation as main ingredients
of radial basis function methods, serve as models for variograms in
statistical methods for spatial data and give rise to the famous kernel trick
used in many methods of machine learning. A reason for the success of
kernel-based methods lies in the intimate conne...
»