title: Recurrence methods in the analysis of learning processes.
abstract: The goal of most learning processes is to bring a machine into a set of "correct" states. In practice, however, it may be difficult to show that the process enters this target set. We present a condition that ensures that the process visits the target set infinitely often almost surely. This condition is easy to verify and is true for many well-known learning rules. To demonstrate the utility of this method, we apply it to four types of learning processes: the perceptron, learning rules governed by continuous energy functions, the Kohonen rule, and the committee machine.
authors.affiliation: Department of Mathematics, Technion, and Institute of Computer Science, Hebrew University, Jerusalem 91120, Israel.
authors.forename: S
authors.initials: S
authors.lastname: Mendelson
authors.forename: I
authors.initials: I
authors.lastname: Nelken
mesh_terms: D000465:Algorithms; D007858:Learning / Q000502:physiology*; D008568:Memory; D008959:Models, Neurological; D008960:Models, Psychological; D016571:Neural Networks, Computer