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Characteristic sets for polynomial grammatical inference

https://doi.org/10.1007/bfb0033342
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What this work says

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

inferredcites →https://doi.org/10.1006/jcss.1995.1003openalex-snapshot
On the Complexity of Teaching
inferredcites →https://doi.org/10.1006/jcss.1996.0020openalex-snapshot
Teaching a Smarter Learner
inferredcites →https://doi.org/10.1007/3-540-51734-0_49openalex-snapshot
Inductive inference from good examples
inferredcites →https://doi.org/10.1007/3-540-51734-0_50openalex-snapshot
Inductive inference, DFAs, and computational complexity
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From inductive inference to algorithmic learning theory
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A hierarchy of language families learnable by regular language learners
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A characterization of even Linear Languages and its application to the learning problem
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Learning strongly deterministic even linear languages from positive examples
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An Introduction to Formal Language Theory
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Introduction to Formal Language Theory
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Queries and Concept Learning
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Teachability in computational learning
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Grammatical inference for even linear languages based on control sets
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Introduction to formal language theory
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LEARNING SIMPLE DETERMINISTIC LANGUAGES
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On the Complexity of Teaching
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Language identification in the limit
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Complexity of automaton identification from given data
inferredcites →https://doi.org/10.1016/s0304-3975(97)00016-9openalex-snapshot
Learning deterministic even linear languages from positive examples
inferredcites →https://doi.org/10.1093/oso/9780198538509.003.0007openalex-snapshot
Learning Non-deterministic Finite Automata from Queries and Counterexamples
inferredcites →https://doi.org/10.1109/34.211465openalex-snapshot
Learning subsequential transducers for pattern recognition interpretation tasks
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INFERRING REGULAR LANGUAGES IN POLYNOMIAL UPDATED TIME
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When won't membership queries help?
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A computational model of teaching
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DNF---if you can't learn'em, teach'em
inferredcites →https://doi.org/10.1145/234752.234755openalex-snapshot
How many queries are needed to learn?

What points at this work

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

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Generalized Graph Colorability and Compressibility of Boolean Formulae
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On the Relationship between Models for Learning in Helpful Environments
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Polynomial-Time Algorithms for Learning Typed Pattern Languages
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Identification of DFA: Data-dependent versus data-independent algorithms
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Approximate learning of random subsequential transducers
inferred← citeshttps://doi.org/10.1016/j.tcs.2003.11.008openalex-snapshot
Learning regular languages using RFSAs
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Learning DFA from Simple Examples
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Learning Regular Languages from Simple Positive Examples
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Regular Language Prediction from Complete Sample in Grammatical Inference Theory – A Literature Survey
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Proceedings of the 16th Meeting on the Mathematics of Language
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Efficient learning of Output Tier-based Strictly 2-Local functions
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Polynomial Identification of $\omega$-Automata
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Learning auxiliary fronting with grammatical inference
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Proceedings of the 14th Meeting on the Mathematics of Language (MoL 2015)
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On Superlinear Speedups of a Parallel NFA Induction Algorithm
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Designing and Learning Substitutable Plane Graph Grammars
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Characteristic Sets for Learning k-Acceptable Languages
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Constructing Concise Characteristic Samples for Acceptors of Omega Regular Languages
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Autómatas finitos: irreducibilidad e Inferencia.
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