From learning in the limit to stochastic finite learning

DOI10.1016/j.tcs.2006.07.042zbMath1153.68022OpenAlexW2119192035MaRDI QIDQ860825

Thomas Zeugmann

Publication date: 9 January 2007

Published in: Theoretical Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.tcs.2006.07.042

zbMATH Keywords

average-case analysisinductive learningstochastic finite learningiterative learningpattern languageslearning in the limitlearning conjunctive concepts

Mathematics Subject Classification ID

Computational learning theory (68Q32)

Related Items (4)

Efficiency in the Identification in the Limit Learning Paradigm ⋮ Developments from enquiries into the learnability of the pattern languages from positive data ⋮ Learning indexed families of recursive languages from positive data: A survey ⋮ Resource restricted computability theoretic learning: Illustrative topics and problems

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Inductive inference of monotonic formal systems from positive data
• Polynomial-time inference of arbitrary pattern languages
• Finding patterns common to a set of strings
• Equivalence of models for polynomial learnability
• Learning elementary formal systems
• Analogical and inductive inference. International workshop AII '89, Reinhardsbrunn Castle, GDR, October 1-6, 1989. Proceedings
• Lange and Wiehagen's pattern language learning algorithm: An average-case analysis with respect to its total learning time
• An average-case optimal one-variable pattern language learner
• Incremental learning from positive data
• Incremental concept learning for bounded data mining.
• Characterizations of monotonic and dual monotonic language learning
• Exact Identification of Read-Once Formulas Using Fixed Points of Amplification Functions
• Theory of Formal Systems. (AM-47)
• Learnability and the Vapnik-Chervonenkis dimension
• A theory of the learnable
• On the complexity of inductive inference
• Inductive inference of formal languages from positive data
• Toward a mathematical theory of inductive inference
• Ignoring data may be the only way to learn efficiently
• Inductive Logic Programming: Theory and methods
• Inclusion is undecidable for pattern languages
• Set-driven and rearrangement-independent learning of recursive languages
• Language identification in the limit
• Inductive inference of unbounded unions of pattern languages from positive data
• Learning one-variable pattern languages very efficiently on average, in parallel, and by asking queries
• Stochastic finite learning of the pattern languages

This page was built for publication: From learning in the limit to stochastic finite learning