Maps for Learning Indexable Classes
Abstract
We study learning of indexed families from positive data where a learner can freely choose a hypothesis space (with uniformly decidable membership) comprising at least the languages to be learned. This abstracts a very universal learning task which can be found in many areas, for example learning of (subsets of) regular languages or learning of natural languages. We are interested in various restrictions on learning, such as consistency, conservativeness or set-drivenness, exemplifying various natural learning restrictions. Building on previous results from the literature, we provide several maps (depictions of all pairwise relations) of various groups of learning criteria, including a map for monotonicity restrictions and similar criteria and a map for restrictions on data presentation. Furthermore, we consider, for various learning criteria, whether learners can be assumed consistent.
Cite
@article{arxiv.2010.09460,
title = {Maps for Learning Indexable Classes},
author = {Julian Berger and Maximilian Böther and Vanja Doskoč and Jonathan Gadea Harder and Nicolas Klodt and Timo Kötzing and Winfried Lötzsch and Jannik Peters and Leon Schiller and Lars Seifert and Armin Wells and Simon Wietheger},
journal= {arXiv preprint arXiv:2010.09460},
year = {2020}
}