  This paper uses deformed coherent states, based on a deformed Weyl-Heisenberg
algebra that unifies the well-known SU(2), Weyl-Heisenberg, and SU(1,1) groups,
through a common parameter. We show that deformed coherent states provide the
theoretical foundation of a meta-kernel function, that is a kernel which in
turn defines kernel functions. Kernel functions drive developments in the field
of machine learning and the meta-kernel function presented in this paper opens
new theoretical avenues for the definition and exploration of kernel functions.
The meta-kernel function applies associated revolution surfaces as feature
spaces identified with non-linear coherent states. An empirical investigation
compares the deformed SU(2) and SU(1,1) kernels derived from the meta-kernel
which shows performance similar to the Radial Basis kernel, and offers new
insights (based on the deformed Weyl-Heisenberg algebra).

Machine Learning;Quantum Physics