Algorithms for approximate string matching
Information and Control
Learning regular sets from queries and counterexamples
Information and Computation
Learning in the presence of malicious errors
SIAM Journal on Computing
When won't membership queries help?
Selected papers of the 23rd annual ACM symposium on Theory of computing
Characteristic Sets for Polynomial Grammatical Inference
Machine Learning
Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
The String-to-String Correction Problem
Journal of the ACM (JACM)
Topology of strings: median string is NP-complete
Theoretical Computer Science
A guided tour to approximate string matching
ACM Computing Surveys (CSUR)
ACM Computing Surveys (CSUR)
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Machine Learning
Machine Learning
Model Generation by Moderated Regular Extrapolation
FASE '02 Proceedings of the 5th International Conference on Fundamental Approaches to Software Engineering
ICGI '98 Proceedings of the 4th International Colloquium on Grammatical Inference
Smoothing Probabilistic Automata: An Error-Correcting Approach
ICGI '00 Proceedings of the 5th International Colloquium on Grammatical Inference: Algorithms and Applications
Approximate String Matching by Finite Automata
CAIP '95 Proceedings of the 6th International Conference on Computer Analysis of Images and Patterns
Theoretical Computer Science - Special issue: Algorithmic learning theory
Interactive learning of node selecting tree transducer
Machine Learning
Algorithms on Strings
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
On Learning Regular Expressions and Patterns Via Membership and Correction Queries
ICGI '08 Proceedings of the 9th international colloquium on Grammatical Inference: Algorithms and Applications
Languages as hyperplanes: grammatical inference with string kernels
ECML'06 Proceedings of the 17th European conference on Machine Learning
Identification in the limit of systematic-noisy languages
ICGI'06 Proceedings of the 8th international conference on Grammatical Inference: algorithms and applications
Learning DFA from correction and equivalence queries
ICGI'06 Proceedings of the 8th international conference on Grammatical Inference: algorithms and applications
String distances and uniformities
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Zulu: an interactive learning competition
FSMNLP'09 Proceedings of the 8th international conference on Finite-state methods and natural language processing
Formal and empirical grammatical inference
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts of ACL 2011
Bio-inspired grammatical inference
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation - Volume Part I
Towards a bio-computational model of natural language learning
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
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When facing the question of learning languages in realistic settings, one has to tackle several problems that do not admit simple solutions. On the one hand, languages are usually defined by complex grammatical mechanisms for which the learning results are predominantly negative, as the few algorithms are not really able to cope with noise. On the other hand, the learning settings themselves rely either on too simple information (text) or on unattainable one (query systems that do not exist in practice, nor can be simulated). We consider simple but sound classes of languages defined via the widely used edit distance: the balls of strings. We propose to learn them with the help of a new sort of queries, called the correction queries: when a string is submitted to the Oracle, either she accepts it if it belongs to the target language, or she proposes a correction, that is, a string of the language close to the query with respect to the edit distance. We show that even if the good balls are not learnable in Angluin's MAT model, they can be learned from a polynomial number of correction queries. Moreover, experimental evidence simulating a human Expert shows that this algorithm is resistant to approximate answers.