On Variants of Iterative Learning

Abstract

Within the present paper, we investigate the principal learning capabilities of iterative learners in some more details. The general scenario of iterative learning is as follows. An iterative learner successively takes as input one element of a text (an informant) of a target concept as well as its previously made hypothesis, and outputs a new hypothesis about the target concept. The sequence of hypotheses has to converge to a hypothesis correctly describing the target concept

We study the following variants of this basic scenario. First, we consider the case that an iterative learner has to learn on redundant texts or informants, only. A text (an informant) is redundant, if it contains every data item infinitely many times. This approach guarantees that relevant information is, in principle, accessible at any time in the learning process. Second, we study a version of iterative learning, where an iterative learner is supposed to learn independent on the choice of the initial hypothesis. In contrast, in the basic scenario of iterative inference, it is assumed that the initial hypothesis is the same for every learning task which allows certain coding tricks

We compare the learning capabilities of all models of iterative learning from text and informant, respectively, to one another as well as to finite inference, conservative identification, and learning in the limit from text and informant, respectively