Inference of regular grammars via skeletons
IEEE Transactions on Systems, Man and Cybernetics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning approximately regular languages with reversible languages
Theoretical Computer Science
Machine learning and data mining
Communications of the ACM
Efficiency of a Good But Not Linear Set Union Algorithm
Journal of the ACM (JACM)
Inference of Reversible Languages
Journal of the ACM (JACM)
Machine Learning
Permutations and Control Sets for Learning Non-regular Language Families
ICGI '00 Proceedings of the 5th International Colloquium on Grammatical Inference: Algorithms and Applications
Forming Grammars for Structured Documents: an Application of Grammatical Inference
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
MFCS '00 Proceedings of the 25th International Symposium on Mathematical Foundations of Computer Science
Approximative Learning of Regular Languages
SOFSEM '01 Proceedings of the 28th Conference on Current Trends in Theory and Practice of Informatics Piestany: Theory and Practice of Informatics
Context and Caterpillars and Structured Documents
PODDP '98 Proceedings of the 4th International Workshop on Principles of Digital Document Processing
On Sufficient Conditions to Identify in the Limit Classes of Grammars from Polynomial Time and Data
ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
Algorithms for Learning Function Distinguishable Regular Languages
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Approximative Learning of Regular Languages
SOFSEM '01 Proceedings of the 28th Conference on Current Trends in Theory and Practice of Informatics Piestany: Theory and Practice of Informatics
Algorithms for learning regular expressions
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
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We sketch possible applications of grammatical inference techniques to problems arising in the context of XML. The idea is to infer document type definitions (DTDs) of XML documents in situations either when the original DTD is missing or when a DTD should be (re)designed or when a DTD should be restricted to a more user-oriented view on a subset of the (given) DTD. The usefulness of such an approach is underlined by the importance of knowing appropriate DTDs; this knowledge can be exploited, e.g., for optimizing database queries based on XML.