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Identification of DFA: Data-dependent versus data-independent algorithms

https://doi.org/10.1007/bfb0033365
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What this work says

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

inferredcites →https://doi.org/10.1007/3-540-51734-0_50openalex-snapshot
Inductive inference, DFAs, and computational complexity
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A hierarchy of language families learnable by regular language learners
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What is the search space of the regular inference?
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A characterization of even Linear Languages and its application to the learning problem
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Regular grammatical inference from positive and negative samples by genetic search: the GIG method
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Learning strongly deterministic even linear languages from positive examples
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Introduction to Formal Language Theory
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Queries and Concept Learning
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Characteristic sets for polynomial grammatical inference
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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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Efficient learning of context-free grammars from positive structural examples
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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
inferredcites →https://doi.org/10.1142/9789812797902_0004openalex-snapshot
INFERRING REGULAR LANGUAGES IN POLYNOMIAL UPDATED TIME
inferredcites →https://doi.org/10.1145/130385.130390openalex-snapshot
Random DFA's can be approximately learned from sparse uniform examples
inferredcites →https://doi.org/10.5860/choice.26-3305openalex-snapshot
An introduction to formal language theory

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

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An Inverse Limit of Context-Free Grammars – A New Approach to Identifiability in the Limit
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Results of the Abbadingo one DFA learning competition and a new evidence-driven state merging algorithm
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The data driven approach applied to the OSTIA algorithm
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Learning stochastic finite automata from experts
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How considering incompatible state mergings may reduce the DFA induction search tree
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Minimal consistent DFA from sample strings
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Learning finite-state models for machine translation
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Natural language grammar induction with a generative constituent-context model
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A bibliographical study of grammatical inference
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A categorical interpretation of state merging algorithms for DFA inference
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Inference of ω-languages from prefixes
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Universal automata and NFA learning
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Polynomial characteristic sets for DFA identification
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On a Construction of Context-free Grammars
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Estudio de la mezcla de estados determinista y no determinista en el diseño de algoritmos para inferencia gramatical de lenguajes regulares.
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Rigorous System-level Modeling and Performance Evaluation for Embedded System Design
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