On the inference of semi-coherent structures from data

Abstract

In this paper, we consider an unknown semi-coherent structure function. Our main focus is the inductive inference problem, that is, how to learn the structure function from data which partially defines the function. We develop a set of algorithms and simulate their success in learning an arbitrary 10-component function, and conclude that the algorithms are feasible.