PAC-learning and asymptotic system identification theory

In this paper we discuss PAC-learning of functions from a traditional system identification perspective. The well established asymptotic theory for the identified models' properties is reviewed from the PAC-learning perspective. The role of finite-dimensional, smooth parametrizations over compact parameter sets is spelled out. This also sets some limits for the interest of identification-theory type results in a learning-theory context.