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Cautious limit learning
V Doskoč, T Kötzing - Algorithmic Learning Theory, 2020 - proceedings.mlr.press
We investigate language learning in the limit from text with various cautious learning
restrictions. Learning is cautious if no hypothesis is a proper subset of a previous guess …
restrictions. Learning is cautious if no hypothesis is a proper subset of a previous guess …
Learning from informants: relations between learning success criteria
M Aschenbach, T Kötzing, K Seidel - ar** monotonic restrictions in inductive inference
V Doskoč, T Kötzing - Connecting with Computability: 17th Conference on …, 2021 - Springer
In inductive inference we investigate computable devices (learners) learning formal
languages. In this work, we focus on monotonic learners which, despite their natural …
languages. In this work, we focus on monotonic learners which, despite their natural …
Normal forms for semantically witness-based learners in inductive inference
V Doskoč, T Kötzing - Connecting with Computability: 17th Conference on …, 2021 - Springer
In inductive inference, we study learners (computable devices) inferring formal languages. In
particular, we consider semantically witness-based learners, that is, learners which are …
particular, we consider semantically witness-based learners, that is, learners which are …
Learning languages with decidable hypotheses
J Berger, M Böther, V Doskoč, JG Harder… - … with Computability: 17th …, 2021 - Springer
In language learning in the limit, the most common type of hypothesis is to give an
enumerator for a language, a W-index. These hypotheses have the drawback that even the …
enumerator for a language, a W-index. These hypotheses have the drawback that even the …
Towards a Map for Incremental Learning in the Limit from Positive and Negative Information
A Khazraei, T Kötzing, K Seidel - … on Computability in Europe, CiE 2021 …, 2021 - Springer
In order to model an efficient learning paradigm, iterative learning algorithms access data
one by one, updating the current hypothesis without regress to past data. Prior research …
one by one, updating the current hypothesis without regress to past data. Prior research …
Maps for Learning Indexable Classes
J Berger, M Böther, V Doskoč, JG Harder… - arxiv preprint arxiv …, 2020 - arxiv.org
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 …
hypothesis space (with uniformly decidable membership) comprising at least the languages …
Learning Languages in the Limit from Positive Information with Finitely Many Memory Changes
T Kötzing, K Seidel - Conference on Computability in Europe, 2021 - Springer
We investigate learning collections of languages from texts by an inductive inference
machine with access to the current datum and a bounded memory in form of states. Such a …
machine with access to the current datum and a bounded memory in form of states. Such a …