Learning deterministic even linear languages from positive examples
Theoretical Computer Science - Special issue on algorithmic learning theory
Inferring pure context-free languages from positive data
Acta Cybernetica
Inductive Inference: Theory and Methods
ACM Computing Surveys (CSUR)
Introduction to the Theory of Computation
Introduction to the Theory of Computation
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
Even linear simple matrix languages: formal language properties and grammatical inference
Theoretical Computer Science
Learning a Subclass of Linear Languages from Positive Structural Information
ICGI '98 Proceedings of the 4th International Colloquium on Grammatical Inference
Learning Locally Testable Even Linear Languages from Positive Data
ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
Learning linear grammars from structural information
ICG! '96 Proceedings of the 3rd International Colloquium on Grammatical Inference: Learning Syntax from Sentences
Inferring Subclasses of Contextual Languages
ICGI '00 Proceedings of the 5th International Colloquium on Grammatical Inference: Algorithms and Applications
An algorithm for constructing an even grammar from a language sample
ACM-SE 42 Proceedings of the 42nd annual Southeast regional conference
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This paper shows that for sample set size two, skeleton based algorithms for inferring even linear grammars either produce a language of the form {w1w2nw3w4nw5 | n ε Z+}. or a language that contains only the original samples. The proof uses the fact that the sample set size is two to create a template for the transitive closure used in generating the inferred grammar.