On Duality in Learning and the Selection of Learning Teams

Abstract

Previous work in inductive inference dealt mostly with finding one or several machines (IIMs) that successfully learn a collection of functions. Herein we start with a class of functions and consider the learner set of all IIMs that are successful at learning the given class. Applying this perspective to the case of team inference leads to the notion of diversification for a class of functions. This enables us to distinguish between several flavors of IIMs all of which must be represented in a team learning the given class. 2 1 Introduction All current theoretical approaches to machine learning tend to focus on a particular machine or a collection of machines and then find the class of concepts which can be learned by these machines under certain constraints defining a criterion of successful learning [AS83, OSW86]. In this paper we investigate the dual problem: Given some set of concepts, which algorithms can learn all those concepts? From [AGS89] we know that in the theory ..