Communications of the ACM
Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
Computational learning theory: an introduction
Computational learning theory: an introduction
Toward efficient agnostic learning
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
How tight are the Vapnik-Chervonenkis bounds?
Neural Computation
Decision theoretic generalizations of the PAC model for neural net and other learning applications
Information and Computation
A result of Vapnik with applications
Discrete Applied Mathematics
On the practical applicability of VC dimension bounds
Neural Computation
PAC-like upper bounds for the sample complexity of leave-one-out cross-validation
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
Algorithmic stability and sanity-check bounds for leave-one-out cross-validation
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
Learning in Neural Networks: Theoretical Foundations
Learning in Neural Networks: Theoretical Foundations
Probabilistic ’generalization‘ of functions and dimension-based uniform convergence results
Statistics and Computing
Function learning from interpolation
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Structural risk minimization over data-dependent hierarchies
IEEE Transactions on Information Theory
Beating the hold-out: bounds for K-fold and progressive cross-validation
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Lower Bounds for Training and Leave-One-Out Estimates of the Generalization Error
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
No Unbiased Estimator of the Variance of K-Fold Cross-Validation
The Journal of Machine Learning Research
Computational Statistics & Data Analysis
A finite-sample simulation study of cross validation in tree-based models
Information Technology and Management
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