Minimization of rational word functions
SIAM Journal on Computing
Characteristic Sets for Polynomial Grammatical Inference
Machine Learning
Learning Subsequential Transducers for Pattern Recognition Interpretation Tasks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Minimizing subsequential transducers: a survey
Theoretical Computer Science
Using domain information during the learning of a subsequential transducer
ICG! '96 Proceedings of the 3rd International Colloquium on Grammatical Inference: Learning Syntax from Sentences
A Generalization of Ginsburg and Rose's Characterization of G-S-M Mappings
Proceedings of the 6th Colloquium, on Automata, Languages and Programming
Interactive learning of node selecting tree transducer
Machine Learning
Deciding equivalence of top--down XML transformations in polynomial time
Journal of Computer and System Sciences
Backward and forward bisimulation minimization of tree automata
Theoretical Computer Science
On relations defined by generalized finite automata
IBM Journal of Research and Development
A learning algorithm for top-down XML transformations
Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Minimization of deterministic bottom-up tree transducers
DLT'10 Proceedings of the 14th international conference on Developments in language theory
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Rational functions are transformations from words to words that can be defined by string transducers. Rational functions are also captured by deterministic string transducers with lookahead. We show for the first time that the class of rational functions can be learned in the limit with polynomial time and data, when represented by string transducers with lookahead in the diagonal-minimal normal form that we introduce.