  We prove a few representer theorems for a localised version of the
regularised and multiview support vector machine learning problem introduced by
H.Q. Minh, L. Bazzani, and V. Murino, Journal of Machine Learning Research,
17(2016) 1-72, that involves operator valued positive semidefinite kernels and
their reproducing kernel Hilbert spaces. The results concern general cases when
convex or nonconvex loss functions and finite or infinite dimensional input
spaces are considered. We show that the general framework allows infinite
dimensional input spaces and nonconvex loss functions for some special cases,
in particular in case the loss functions are Gateaux differentiable. Detailed
calculations are provided for the exponential least square loss function that
lead to partially nonlinear equations for which a particular unconstrained
potential reduction Newton's approximation method can be used.
