Communications of the ACM
On the learnability and usage of acyclic probabilistic finite automata
COLT '95 Proceedings of the eighth annual conference on 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
Stochastic Grammatical Inference of Text Database Structure
Machine Learning
ICGI '98 Proceedings of the 4th International Colloquium on Grammatical Inference
Learning Context-Free Grammars from Partially Structured Examples
ICGI '00 Proceedings of the 5th International Colloquium on Grammatical Inference: Algorithms and Applications
Learning Stochastic Regular Grammars by Means of a State Merging Method
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
What Is the Search Space of the Regular Inference?
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
Identification of DFA: data-dependent vs data-independent algorithms
ICG! '96 Proceedings of the 3rd International Colloquium on Grammatical Inference: Learning Syntax from Sentences
MLDM '01 Proceedings of the Second International Workshop on Machine Learning and Data Mining in Pattern Recognition
Identification of Function Distinguishable Languages
ALT '00 Proceedings of the 11th International Conference on Algorithmic Learning Theory
Inference of W-languages from prefixes
Theoretical Computer Science - Special issue: Algorithmic learning theory
Probabilistic Finite-State Machines-Part II
IEEE Transactions on Pattern Analysis and Machine Intelligence
Interactive learning of node selecting tree transducer
Machine Learning
How to Split Recursive Automata
ICGI '08 Proceedings of the 9th international colloquium on Grammatical Inference: Algorithms and Applications
A bibliographical study of grammatical inference
Pattern Recognition
Conversation mining in multi-agent systems
CEEMAS'03 Proceedings of the 3rd Central and Eastern European conference on Multi-agent systems
Ten open problems in grammatical inference
ICGI'06 Proceedings of the 8th international conference on Grammatical Inference: algorithms and applications
Learning stochastic finite automata for musical style recognition
CIAA'05 Proceedings of the 10th international conference on Implementation and Application of Automata
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Grammatical inference consists in learning formal grammars for unknown languages when given learning data. Classically this data is raw: strings that belong to the language and eventually strings that do not. We present in this paper the possibility of learning when presented with additional information such as the knowledge that the hidden language belongs to some known language, or that the strings are typed, or that specific patterns have to/can appear in the strings. We propose a general setting to deal with these cases and provide algorithms that can learn deterministic finite automata in these conditions. Furthermore the number of examples needed to correctly identify can diminish drastically with the quality of the added information. We show that this general setting can cope with several well known learning tasks.