Kernel-based learning with guarantees for multi-agent applications
Krzysztof Kowalczyk Paweł Wachel Cristian R. Rojas
This paper addresses a kernel-based learning problem for a network of agents
locally observing a latent multidimensional, nonlinear phenomenon in a noisy
environment. We propose a learning algorithm that requires only mild a priori
knowledge about the phenomenon under investigation and delivers a model with
corresponding non-asymptotic high probability error bounds. Both non-asymptotic
analysis of the method and numerical simulation results are presented and
discussed in the paper.