Communications of the ACM
Learning regular sets from queries and counterexamples
Information and Computation
A Necessary Condition for Learning from Positive Examples
Machine Learning
Probably approximate learning of sets and functions
SIAM Journal on Computing
Machine learning: a theoretical approach
Machine learning: a theoretical approach
Learning simple concepts under simple distributions
SIAM Journal on Computing
Equivalence of models for polynomial learnability
Information and Computation
An introduction to Kolmogorov complexity and its applications
An introduction to Kolmogorov complexity and its applications
Efficient learning of context-free grammars from positive structural examples
Information and Computation
Cryptographic limitations on learning Boolean formulae and finite automata
Journal of the ACM (JACM)
An introduction to computational learning theory
An introduction to computational learning theory
Journal of Computer and System Sciences
Angluin's theorem for indexed families of r.e. sets and applications
COLT '96 Proceedings of the ninth 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
Learning deterministic even linear languages from positive examples
Theoretical Computer Science - Special issue on algorithmic learning theory
Inference of Reversible Languages
Journal of the ACM (JACM)
ICGI '98 Proceedings of the 4th International Colloquium on Grammatical Inference
Learning Stochastic Regular Grammars by Means of a State Merging Method
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
Inducing Probabilistic Grammars by Bayesian Model Merging
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
PAC Learning with Simple Examples
STACS '96 Proceedings of the 13th Annual Symposium on Theoretical Aspects of Computer Science
Inductive Inference, DFAs, and Computational Complexity
AII '89 Proceedings of the International Workshop on Analogical and Inductive Inference
PAC Learning under Helpful Distributions
ALT '97 Proceedings of the 8th International Conference on Algorithmic Learning Theory
Learning DFA from Simple Examples
ALT '97 Proceedings of the 8th International Conference on Algorithmic Learning Theory
Exploring Learnability between Exact and PAC
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
PAC-learnability of Probabilistic Deterministic Finite State Automata
The Journal of Machine Learning Research
Exploring learnability between exact and PAC
Journal of Computer and System Sciences - Special issue on COLT 2002
Alternative approaches for generating bodies of grammar rules
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Partially distribution-free learning of regular languages from positive samples
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Discoverer: automatic protocol reverse engineering from network traces
SS'07 Proceedings of 16th USENIX Security Symposium on USENIX Security Symposium
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
Regular expression learning for information extraction
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
A heuristic approach for computing effects
TOOLS'11 Proceedings of the 49th international conference on Objects, models, components, patterns
Learning regular expressions from noisy sequences
SARA'05 Proceedings of the 6th international conference on Abstraction, Reformulation and Approximation
Improving recall of regular expressions for information extraction
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
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Learning from positive data constitutes an important topic in Grammatical Inference since it is believed that the acquisition of grammar by children only needs syntactically correct (i.e. positive) instances. However, classical learning models provide no way to avoid the problem of overgeneralization. In order to overcome this problem, we use here a learning model from simple examples, where the notion of simplicity is defined with the help of Kolmogorov complexity. We show that a general and natural heuristic which allows learning from simple positive examples can be developed in this model. Our main result is that the class of regular languages is probably exactly learnable from simple positive examples.