Second-order inductive learning

Abstract

In this paper, we present a new paradigm for inductive learning, called second-order inductive learning. It differs from concept learning from examples in that examples are not instances of the hypothesis to be learned, but rather instances of a prototype (i.e., a typical member of the extension) of the hypothesis to be learned. The paradigm is introduced by means of an example problem from the field of conceptual modeling. We analyse the reasons why a naive solution to that problem is not fully satisfactory by studying the Version Space model. Once it is clear why this model is not directly applicable, we attempt to restore it by defining the notion of a Generalised Version Space model.