Efficient learning of context-free grammars from positive structural examples
Information and Computation
A note on the grammatical inference problem for even linear languages
Fundamenta Informaticae
Recent advances of grammatical inference
Theoretical Computer Science - Special issue on algorithmic learning theory
Learning deterministic even linear languages from positive examples
Theoretical Computer Science - Special issue on algorithmic learning theory
Limits of pure grammars with monotone productions
Fundamenta Informaticae - Special issue on grammar systems
Regular Grammatical Inference from Positive and Negative Samples by Genetic Search: the GIG Method
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
Identification of DFA: data-dependent vs data-independent algorithms
ICG! '96 Proceedings of the 3rd International Colloquium on Grammatical Inference: Learning Syntax from Sentences
Learning linear grammars from structural information
ICG! '96 Proceedings of the 3rd International Colloquium on Grammatical Inference: Learning Syntax from Sentences
Formal languages and their relation to automata
Formal languages and their relation to automata
The Mathematical Theory of Context-Free Languages
The Mathematical Theory of Context-Free Languages
Construction of Contextual Grammars
Fundamenta Informaticae - Contagious Creativity - In Honor of the 80th Birthday of Professor Solomon Marcus
Construction of Contextual Grammars
Fundamenta Informaticae - Contagious Creativity - In Honor of the 80th Birthday of Professor Solomon Marcus
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The grammatical inference problem is solved for the class of context-free languages. A context-free language is supposed to be given by means of all its strings. Considering all strings of length bounded by k, context-free grammars G_{j,k} with 1≤j