Grammatical interface for even linear languages based on control sets
Information Processing Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning simple deterministic languages
COLT '89 Proceedings of the second annual workshop on Computational learning theory
Teachability in computational learning
New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
On exact specification by examples
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
A computational model of teaching
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Journal of Computer and System Sciences
When won't membership queries help?
Selected papers of the 23rd annual ACM symposium on Theory of computing
Learning non-deterministic finite automata from queries and counterexamples
Machine intelligence 13
DNF—if you can't learn'em, teach'em: an interactive model of teaching
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Journal of Computer and System Sciences
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Introduction to Formal Language Theory
Introduction to Formal Language Theory
Learning Subsequential Transducers for Pattern Recognition Interpretation Tasks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Machine Learning
A Hierarchy of Language Families Learnable by Regular Language Learners
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
Application of OSTIA to Machine Translation Tasks
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
Inductive Inference, DFAs, and Computational Complexity
AII '89 Proceedings of the International Workshop on Analogical and Inductive Inference
Inductive Inference from Good Examples
AII '89 Proceedings of the International Workshop on Analogical and Inductive Inference
From Inductive Inference to Algorithmic Learning Theory
ALT '92 Proceedings of the Third Workshop on Algorithmic Learning Theory
Learning Strongly Deterministic Even Linear Languages from Positive Examples
ALT '95 Proceedings of the 6th International Conference on Algorithmic Learning Theory
A Characterization of Even Linear Languages and its Application to the Learning Problem
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
Learning Regular Languages from Simple Positive Examples
Machine Learning
On Sufficient Conditions to Identify in the Limit Classes of Grammars from Polynomial Time and Data
ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
ICGI '02 Proceedings of the 6th 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
Inference of omega-Languages from Prefixes
ALT '01 Proceedings of the 12th International Conference on Algorithmic Learning Theory
Learning Regular Languages Using RFSA
ALT '01 Proceedings of the 12th International Conference on Algorithmic Learning Theory
Inferring Deterministic Linear Languages
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
Learning regular languages using RFSAs
Theoretical Computer Science - Special issue: Algorithmic learning theory
Inference of W-languages from prefixes
Theoretical Computer Science - Special issue: Algorithmic learning theory
Probabilistic Finite-State Machines-Part I
IEEE Transactions on Pattern Analysis and Machine Intelligence
LARS: A learning algorithm for rewriting systems
Machine Learning
Interactive learning of node selecting tree transducer
Machine Learning
Residual Finite State Automata
Fundamenta Informaticae
Learning indexed families of recursive languages from positive data: A survey
Theoretical Computer Science
Learning Efficiency of Very Simple Grammars from Positive Data
ALT '07 Proceedings of the 18th international conference on Algorithmic Learning Theory
A Polynomial Algorithm for the Inference of Context Free Languages
ICGI '08 Proceedings of the 9th international colloquium on Grammatical Inference: Algorithms and Applications
Learning Languages from Bounded Resources: The Case of the DFA and the Balls of Strings
ICGI '08 Proceedings of the 9th international colloquium on Grammatical Inference: Algorithms and Applications
Learning Left-to-Right and Right-to-Left Iterative Languages
ICGI '08 Proceedings of the 9th international colloquium on Grammatical Inference: Algorithms and Applications
Polynomial Time Probabilistic Learning of a Subclass of Linear Languages with Queries
ICGI '08 Proceedings of the 9th international colloquium on Grammatical Inference: Algorithms and Applications
Polynomial Distinguishability of Timed Automata
ICGI '08 Proceedings of the 9th international colloquium on Grammatical Inference: Algorithms and Applications
Identification in the Limit of k,l-Substitutable Context-Free Languages
ICGI '08 Proceedings of the 9th international colloquium on Grammatical Inference: Algorithms and Applications
Learning Balls of Strings from Edit Corrections
The Journal of Machine Learning Research
Learning efficiency of very simple grammars from positive data
Theoretical Computer Science
One-Clock Deterministic Timed Automata Are Efficiently Identifiable in the Limit
LATA '09 Proceedings of the 3rd International Conference on Language and Automata Theory and Applications
Learning auxiliary fronting with grammatical inference
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
A bibliographical study of grammatical inference
Pattern Recognition
ALT'09 Proceedings of the 20th international conference on Algorithmic learning theory
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Learning finite state machines
FSMNLP'09 Proceedings of the 8th international conference on Finite-state methods and natural language processing
The efficiency of identifying timed automata and the power of clocks
Information and Computation
Using Contextual Representations to Efficiently Learn Context-Free Languages
The Journal of Machine Learning Research
Theoretical Computer Science
Formal and empirical grammatical inference
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts of ACL 2011
Identification in the limit of substitutable context-free languages
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
Inductive inference and language learning
TAMC'06 Proceedings of the Third international conference on Theory and Applications of Models of Computation
Ten open problems in grammatical inference
ICGI'06 Proceedings of the 8th international conference on Grammatical Inference: algorithms and applications
Learning n-ary node selecting tree transducers from completely annotated examples
ICGI'06 Proceedings of the 8th international conference on Grammatical Inference: algorithms and applications
Finding the most probable string and the consensus string: an algorithmic study
IWPT '11 Proceedings of the 12th International Conference on Parsing Technologies
Learning twig and path queries
Proceedings of the 15th International Conference on Database Theory
Polynomial characteristic sets for DFA identification
Theoretical Computer Science
DLT'12 Proceedings of the 16th 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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When concerned about efficient grammatical inference two issues arerelevant: the first one is to determine the quality of the result,and the second is to try to use polynomial time and space. A typicalidea to deal with the first point is to say that an algorithmperforms well if it infers {\it in\ the\ limit} the correctlanguage. The second point has led to debate about how to definepolynomial time: the main definitions of polynomial inference havebeen proposed by Pitt and Angluin. We return in this paper to adefinition proposed by Gold that requires a characteristic set ofstrings to exist for each grammar, and this set to be polynomial inthe size of the grammar or automaton that is to be learned, where thesize of the sample is the sum of the lengths of all strings itincludes. The learning algorithm must also infer correctly as soon asthe characteristic set is included in the data. We first show thatthis definition corresponds to a notion of teachability as defined byGoldman and Mathias. By adapting their teacher/learner model togrammatical inference we prove that languages given by context-freegrammars, simple deterministic grammars, linear grammars andnondeterministic finite automata are not identifiable in the limitfrom polynomial time and data.