LEARNING RECURSIVE LANGUAGES WITH BOUNDED MIND CHANGES

DOI10.1142/S0129054193000110zbMath0802.68105OpenAlexW2092925110MaRDI QIDQ4286118

Steffen Lange, Thomas Zeugmann

Publication date: 18 December 1994

Published in: International Journal of Foundations of Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1142/s0129054193000110

zbMATH Keywords

learning theoryautomata and formal languagesfamilies of uniformly recursive languages

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Formal languages and automata (68Q45)

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Intrinsic complexity of learning geometrical concepts from positive data ⋮ Generalized notions of mind change complexity ⋮ Probabilistic language learning under monotonicity constraints ⋮ Monotonic and dual monotonic language learning ⋮ Towards a mathematical theory of machine discovery from facts ⋮ Characterizing language identification in terms of computable numberings ⋮ Hypothesis spaces for learning ⋮ Learnability and positive equivalence relations ⋮ Hypothesis Spaces for Learning ⋮ Set-driven and rearrangement-independent learning of recursive languages ⋮ Learning languages and functions by erasing ⋮ Incremental concept learning for bounded data mining. ⋮ Language learning from texts: Degrees of intrinsic complexity and their characterizations

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