The minimum consistent DFA problem cannot be approximated within and polynomial
STOC '89 Proceedings of the twenty-first annual ACM symposium on Theory of computing
A tutorial on hidden Markov models and selected applications in speech recognition
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SIAM Journal on Computing
Theoretical Computer Science
Journal of Computer and System Sciences
Characteristic Sets for Polynomial Grammatical Inference
Machine Learning
Event-clock automata: a determinizable class of timed automata
Theoretical Computer Science
Journal of the ACM (JACM)
Introduction to the Theory of Computation: Preliminary Edition
Introduction to the Theory of Computation: Preliminary Edition
ICGI '98 Proceedings of the 4th International Colloquium on Grammatical Inference
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Inference of event-recording automata using timed decision trees
CONCUR'06 Proceedings of the 17th international conference on Concurrency Theory
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We develop theory on the efficiency of identifying (learning) timed automata. In particular, we show that: (i) deterministic timed automata cannot be identified efficiently in the limit from labeled data and (ii) that one-clock deterministic timed automata can be identified efficiently in the limit from labeled data. We prove these results based on the distinguishability of these classes of timed automata. More specifically, we prove that the languages of deterministic timed automata cannot, and that one-clock deterministic timed automata can be distinguished from each other using strings in length bounded by a polynomial. In addition, we provide an algorithm that identifies one-clock deterministic timed automata efficiently from labeled data. Our results have interesting consequences for the power of clocks that are interesting also out of the scope of the identification problem.