Grammatical interface for even linear languages based on control sets
Information Processing Letters
Learning context-free grammars from structural data in polynomial time
Theoretical Computer Science
Efficient learning of context-free grammars from positive structural examples
Information and Computation
Handbook of Formal Languages
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 Deterministic Linear Languages
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
A Characterization of Even Linear Languages and its Application to the Learning Problem
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
Counter-Free Automata (M.I.T. research monograph no. 65)
Counter-Free Automata (M.I.T. research monograph no. 65)
The Relations among Watson-Crick Automata and Their Relations with Context-Free Languages
IEICE - Transactions on Information and Systems
DNA Computing: New Computing Paradigms (Texts in Theoretical Computer Science. An EATCS Series)
DNA Computing: New Computing Paradigms (Texts in Theoretical Computer Science. An EATCS Series)
Natural Computing: an international journal
Journal of Computer and System Sciences
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In this work we propose a method to infer context-sensitive languages from positive structural examples produced by linear grammars. Our approach is based on a representation theorem induced by two operations over strings: duplication and reversal. The inference method produces an acceptor device which is an unconventional model of computation based on biomolecules (DNA computing). We prove that a subclass of context-sensitive languages can be inferred by using the representation result in combination with reductions from linear languages to k-testable in the strict sense regular languages.