Communications of the ACM
Computational limitations on learning from examples
Journal of the ACM (JACM)
Crytographic limitations on learning Boolean formulae and finite automata
STOC '89 Proceedings of the twenty-first annual ACM symposium on Theory of computing
Negative Results for Equivalence Queries
Machine Learning
Learning simple concepts under simple distributions
SIAM Journal on Computing
Equivalence of models for polynomial learnability
Information and Computation
The minimum consistent DFA problem cannot be approximated within any polynomial
Journal of the ACM (JACM)
An introduction to computational learning theory
An introduction to computational learning theory
Characteristic Sets for Polynomial Grammatical Inference
Machine Learning
The String-to-String Correction Problem
Journal of the ACM (JACM)
The Complexity of Some Problems on Subsequences and Supersequences
Journal of the ACM (JACM)
Topology of strings: median string is NP-complete
Theoretical Computer Science
Inductive Inference: Theory and Methods
ACM Computing Surveys (CSUR)
A guided tour to approximate string matching
ACM Computing Surveys (CSUR)
ACM Computing Surveys (CSUR)
Learning Regular Languages from Simple Positive Examples
Machine Learning
Machine Learning
Machine Learning
On the Relationship between Models for Learning in Helpful Environments
ICGI '00 Proceedings of the 5th International Colloquium on Grammatical Inference: Algorithms and Applications
Inductive Inference, DFAs, and Computational Complexity
AII '89 Proceedings of the International Workshop on Analogical and Inductive Inference
Towards Representation Independence in PAC Learning
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
Learning Balls of Strings with Correction Queries
ECML '07 Proceedings of the 18th European conference on Machine Learning
Learning Balls of Strings from Edit Corrections
The Journal of Machine Learning Research
Sequences classification by least general generalisations
ICGI'10 Proceedings of the 10th international colloquium conference on Grammatical inference: theoretical results and applications
Learning finite state machines
FSMNLP'09 Proceedings of the 8th international conference on Finite-state methods and natural language processing
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
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Comparison of standard language learning paradigms (identification in the limit, query learning, Paclearning) has always been a complex question. Moreover, when to the question of converging to a target one adds computational constraints, the picture becomes even less clear: how much do queries or negative examples help? Can we find good algorithms that change their minds very little or that make very few errors? In order to approach these problems we concentrate here on two classes of languages, the topological balls of strings (for the edit distance) and the deterministic finite automata (), and (re-)visit the different learning paradigms to sustain our claims.