Communications of the ACM
Learning regular sets from queries and counterexamples
Information and Computation
Learning from good and bad data
Learning from good and bad data
A general lower bound on the number of examples needed for learning
Information and Computation
Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
On the sample complexity of pac-learning using random and chosen examples
COLT '90 Proceedings of the third annual workshop on Computational learning theory
Learning k-DNF with noise in the attributes
COLT '88 Proceedings of the first annual workshop on Computational learning theory
On learning from queries and counterexamples in the presence of noise
Information Processing Letters
Learnability with respect to fixed distributions
Theoretical Computer Science
Elements of information theory
Elements of information theory
Learning with a slowly changing distribution
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Learning in the presence of malicious errors
SIAM Journal on Computing
Lower bounds for PAC learning with queries
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
Bounds on the Sample Complexity of Bayesian Learning Using Information Theory and the VC Dimension
Machine Learning - Special issue on computational learning theory
Predicting {0, 1}-functions on randomly drawn points
Information and Computation
Four types of noise in data for PAC learning
Information Processing Letters
The nature of statistical learning theory
The nature of statistical learning theory
On the sample complexity of noise-tolerant learning
Information Processing Letters
General bounds on the number of examples needed for learning probabilistic concepts
Journal of Computer and System Sciences
Malicious Omissions and Errors in Answers to Membership Queries
Machine Learning
On the complexity of learning from drifting distributions
Information and Computation
Sample size lower bounds in PAC learning by algorithmic complexity theory
Theoretical Computer Science
Machine Learning
Machine Learning
Machine Learning
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Lower bounds on expected redundancy for nonparametric classes
IEEE Transactions on Information Theory
Sample-efficient strategies for learning in the presence of noise
Journal of the ACM (JACM)
Active learning in the non-realizable case
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
Distribution-dependent sample complexity of large margin learning
The Journal of Machine Learning Research
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