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arXiv · 2010.09460

Maps for Learning Indexable Classes

Julian Berger·Maximilian Böther·Vanja Doskoč·Jonathan Gadea Harder·Nicolas Klodt·Timo Kötzing·Winfried Lötzsch·Jannik Peters·Leon Schiller·Lars Seifert·Armin Wells·Simon Wietheger
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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.

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Julian Berger, Maximilian Böther, Vanja Doskoč, Jonathan Gadea Harder, Nicolas Klodt, Timo Kötzing, Winfried Lötzsch, Jannik Peters, Leon Schiller, Lars Seifert, Armin Wells, Simon Wietheger. 2020-10-15. Maps for Learning Indexable Classes. https://arxiv.org/abs/2010.09460

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