Characteristic Sets for Polynomial Grammatical Inference
Machine Learning
Handbook of formal languages, vol. 1
Derivatives of Regular Expressions
Journal of the ACM (JACM)
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
ICGI '98 Proceedings of the 4th International Colloquium on Grammatical Inference
Efficient Ambiguity Detection in C-NFA, a Step Towards the Inference on Non Deterministic Automata
ICGI '00 Proceedings of the 5th International Colloquium on Grammatical Inference: Algorithms and Applications
Learning Regular Languages Using Non Deterministic Finite Automata
ICGI '00 Proceedings of the 5th International Colloquium on Grammatical Inference: Algorithms and Applications
Residual Finite State Automata
STACS '01 Proceedings of the 18th Annual Symposium on Theoretical Aspects of Computer Science
Learning Probabilistic Residual Finite State Automata
ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
Some Classes of Regular Languages Identifiable in the Limit from Positive Data
ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
Residual Finite State Automata
Fundamenta Informaticae
A bibliographical study of grammatical inference
Pattern Recognition
DLT'03 Proceedings of the 7th international conference on Developments in language theory
Residual languages and probabilistic automata
ICALP'03 Proceedings of the 30th international conference on Automata, languages and programming
Inference of residual finite-state tree automata from membership queries and finite positive data
DLT'11 Proceedings of the 15th international conference on Developments in language theory
Residual Finite State Automata
Fundamenta Informaticae
Four one-shot learners for regular tree languages and their polynomial characterizability
Theoretical Computer Science
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Residual languages are important and natural components of regular languages. Most approaches in grammatical inference rely on this notion. Classical algorithms such as RPNI try to identify prefixes of positive learning examples which give rise to identical residual languages. Here, we study inclusion relations between residual languages. We lead experiments which show that when regular languages are randomly drawn using non deterministicrepresen tations, the number of inclusion relations is very important. We introduced in previous articles a new class of automata which is defined using the notion of residual languages: residual finite state automata (RFSA). RFSA representations of regular languages may have far less states than DFA representations. We prove that RFSA are not polynomially characterizable. However, we design a new learning algorithm, DeLeTe2, based on the search of inclusion relations between residual languages, which produces a RFSA and have both good theoretical properties and good experimental performances.