Learning regular sets from queries and counterexamples
Information and Computation
Random DFA's can be approximately learned from sparse uniform examples
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
An introduction to computational learning theory
An introduction to computational learning theory
Characteristic Sets for Polynomial Grammatical Inference
Machine Learning
Recent advances of grammatical inference
Theoretical Computer Science - Special issue on algorithmic learning theory
Inductive Inference: Theory and Methods
ACM Computing Surveys (CSUR)
Machine Learning
Machine Learning
ICGI '98 Proceedings of the 4th International Colloquium on Grammatical Inference
Identification of DFA: data-dependent vs data-independent algorithms
ICG! '96 Proceedings of the 3rd International Colloquium on Grammatical Inference: Learning Syntax from Sentences
Inductive Inference, DFAs, and Computational Complexity
AII '89 Proceedings of the International Workshop on Analogical and Inductive Inference
Learning regular languages using RFSAs
Theoretical Computer Science - Special issue: Algorithmic learning theory
Universal automata and NFA learning
Theoretical Computer Science
A bibliographical study of grammatical inference
Pattern Recognition
Grammatical Inference: Learning Automata and Grammars
Grammatical Inference: Learning Automata and Grammars
ICGI'10 Proceedings of the 10th international colloquium conference on Grammatical inference: theoretical results and applications
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We study the order in Grammatical Inference algorithms, and its influence on the polynomial (with respect to the data) identification of languages. This work is motivated by recent results on the polynomial convergence of data-driven grammatical inference algorithms. In this paper, we prove a sufficient condition that assures the existence of a characteristic sample whose size is polynomial with respect to the minimum DFA of the target language.