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Set-driven and rearrangement-independent learning of recursive languages

https://doi.org/10.1007/bf01301967
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/3-540-57369-0_33openalex-snapshot
Inductive inference with bounded mind changes
inferredcites →https://doi.org/10.1007/3-540-60217-8_12openalex-snapshot
A guided tour across the boundaries of learning recursive languages
inferredcites →https://doi.org/10.1007/978-94-011-1840-8_10openalex-snapshot
The Subset Principle is an Intensional Principle
inferredcites →https://doi.org/10.1007/bf03037092openalex-snapshot
Monotonic and non-monotonic inductive inference
inferredcites →https://doi.org/10.1007/bf03037093openalex-snapshot
Polynomial-time inference of arbitrary pattern languages
inferredcites →https://doi.org/10.1007/bfb0023324openalex-snapshot
A thesis in inductive inference
inferredcites →https://doi.org/10.1007/bfb0030397openalex-snapshot
Monotonic versus non-monotonic language learning
inferredcites →https://doi.org/10.1016/0022-0000(80)90041-0openalex-snapshot
Finding patterns common to a set of strings
inferredcites →https://doi.org/10.1016/0304-3975(95)00284-7openalex-snapshot
Monotonic and dual monotonic language learning
inferredcites →https://doi.org/10.1016/0378-4754(79)90040-5openalex-snapshot
An introduction to the general theory of algorithms
inferredcites →https://doi.org/10.1016/0890-5401(90)90042-gopenalex-snapshot
Prudence and other conditions on formal language learning
inferredcites →https://doi.org/10.1016/s0019-9958(67)91165-5openalex-snapshot
Language identification in the limit
inferredcites →https://doi.org/10.1016/s0019-9958(75)90261-2openalex-snapshot
Toward a mathematical theory of inductive inference
inferredcites →https://doi.org/10.1016/s0019-9958(80)90285-5openalex-snapshot
Inductive inference of formal languages from positive data
inferredcites →https://doi.org/10.1016/s0736-5853(86)80070-3openalex-snapshot
The Acquisition of Syntactic Knowledge
inferredcites →https://doi.org/10.1080/09528139408953782openalex-snapshot
Characterization of language learning front informant under various monotonicity constraints
inferredcites →https://doi.org/10.1080/09528139408953785openalex-snapshot
Ignoring data may be the only way to learn efficiently
inferredcites →https://doi.org/10.1142/s0129054193000110openalex-snapshot
LEARNING RECURSIVE LANGUAGES WITH BOUNDED MIND CHANGES
inferredcites →https://doi.org/10.1145/130385.130427openalex-snapshot
Types of monotonic language learning and their characterization
inferredcites →https://doi.org/10.1145/168304.168320openalex-snapshot
Language learning in dependence on the space of hypotheses
inferredcites →https://doi.org/10.13182/nse70-a19706openalex-snapshot
Formal Languages and their Relation to Automata
inferredcites →https://doi.org/10.2307/414105openalex-snapshot
Formal principles of language acquisition . By Kenneth Wexler and Peter W. Culicover. Cambridge, MA: MIT Press, 1980. Pp. xvii, 647. $35.00.
inferredcites →https://doi.org/10.7551/mitpress/1074.001.0001openalex-snapshot
The Acquisition of Syntactic Knowledge

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Incremental Concept Learning for Bounded Data Mining
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Incremental Learning from Positive Data
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An Average-Case Optimal One-Variable Pattern Language Learner
inferred← citeshttps://doi.org/10.1007/3-540-40992-0_8openalex-snapshot
Learning Recursive Concepts with Anomalies
inferred← citeshttps://doi.org/10.1007/3-540-45322-9_11openalex-snapshot
Stochastic Finite Learning
inferred← citeshttps://doi.org/10.1007/3-540-49292-5_7openalex-snapshot
On Variants of Iterative Learning
inferred← citeshttps://doi.org/10.1007/3-540-60217-8_12openalex-snapshot
A guided tour across the boundaries of learning recursive languages
inferred← citeshttps://doi.org/10.1007/3-540-63577-7_48openalex-snapshot
Learning one-variable pattern languages very efficiently on average, in parallel, and by asking queries
inferred← citeshttps://doi.org/10.1007/978-0-387-30164-8_787openalex-snapshot
Stochastic Finite Learning
inferred← citeshttps://doi.org/10.1007/978-1-4899-7502-7_793-1openalex-snapshot
Stochastic Finite Learning
inferred← citeshttps://doi.org/10.1007/978-1-4899-7687-1_793openalex-snapshot
Stochastic Finite Learning
inferred← citeshttps://doi.org/10.1007/978-3-030-80049-9_13openalex-snapshot
Mapping Monotonic Restrictions in Inductive Inference
inferred← citeshttps://doi.org/10.1007/978-3-540-39624-6_5openalex-snapshot
Can Learning in the Limit Be Done Efficiently?
inferred← citeshttps://doi.org/10.1007/978-3-540-72927-3_14openalex-snapshot
U-Shaped, Iterative, and Iterative-with-Counter Learning
inferred← citeshttps://doi.org/10.1007/978-3-642-00982-2_4openalex-snapshot
Hypothesis Spaces for Learning
inferred← citeshttps://doi.org/10.1007/978-3-642-28332-1_22openalex-snapshot
Learnability of Co-r.e. Classes
inferred← citeshttps://doi.org/10.1007/978-3-662-59565-7_3openalex-snapshot
Finite Identification with Positive and with Complete Data
inferred← citeshttps://doi.org/10.1007/s10994-008-5047-9openalex-snapshot
U-shaped, iterative, and iterative-with-counter learning
inferred← citeshttps://doi.org/10.1016/j.ic.2003.11.001openalex-snapshot
Generalized notions of mind change complexity
inferred← citeshttps://doi.org/10.1016/j.ic.2008.12.008openalex-snapshot
Algorithms for learning regular expressions from positive data
inferred← citeshttps://doi.org/10.1016/j.ic.2010.11.016openalex-snapshot
Hypothesis spaces for learning
inferred← citeshttps://doi.org/10.1016/j.tcs.2005.09.004openalex-snapshot
Inductive inference of approximations for recursive concepts
inferred← citeshttps://doi.org/10.1016/j.tcs.2006.07.042openalex-snapshot
From learning in the limit to stochastic finite learning
inferred← citeshttps://doi.org/10.1016/j.tcs.2008.02.030openalex-snapshot
Learning indexed families of recursive languages from positive data: A survey
inferred← citeshttps://doi.org/10.1016/s0304-3975(00)00136-5openalex-snapshot
Learning one-variable pattern languages very efficiently on average, in parallel, and by asking queries
inferred← citeshttps://doi.org/10.1016/s0304-3975(02)00176-7openalex-snapshot
Variants of iterative learning
inferred← citeshttps://doi.org/10.1023/a:1010875913047openalex-snapshot
Stochastic Finite Learning of the Pattern Languages
inferred← citeshttps://doi.org/10.1023/a:1018964207937openalex-snapshot
Lange and Wiehagen's pattern language learning algorithm: An average-case analysis with respect to its total learning time
inferred← citeshttps://doi.org/10.1093/comjnl/bxs059openalex-snapshot
On the Complexity of Conclusive Update
inferred← citeshttps://doi.org/10.2307/j.ctv22jnmh8.8openalex-snapshot
Knowing One’s Limits
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Maps for learning indexable classes
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