Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
Computational learning theory: survey and selected bibliography
STOC '92 Proceedings of the twenty-fourth annual ACM symposium on Theory of computing
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
A noise model on learning sets of strings
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Corrigendum to types of noise in data for concept learning
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Efficient noise-tolerant learning from statistical queries
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
Learning fallible finite state automata
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
Learning with queries but incomplete information (extended abstract)
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Learning from a consistently ignorant teacher
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Learning with unreliable boundary queries
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
On learning from noisy and incomplete examples
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Learning from examples with unspecified attribute values (extended abstract)
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
Efficient noise-tolerant learning from statistical queries
Journal of the ACM (JACM)
Learning fixed-dimension linear thresholds from fragmented data
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Learning fixed-dimension linear thresholds from fragmented data
Information and Computation
On using extended statistical queries to avoid membership queries
The Journal of Machine Learning Research
A study of the effect of different types of noise on the precision of supervised learning techniques
Artificial Intelligence Review
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