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)
The learnability of formal concepts
COLT '90 Proceedings of the third annual workshop on Computational learning theory
Learnability with respect to fixed distributions
Theoretical Computer Science
Equivalence of models for polynomial learnability
Information and Computation
Toward efficient agnostic learning
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Decision theoretic generalizations of the PAC model for neural net and other learning applications
Information and Computation
Fat-shattering and the learnability of real-valued functions
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Predicting {0, 1}-functions on randomly drawn points
Information and Computation
Sphere packing numbers for subsets of the Boolean n-cube with bounded Vapnik-Chervonenkis dimension
Journal of Combinatorial Theory Series A
Bounds on the number of examples needed for learning functions
Euro-COLT '93 Proceedings of the first European conference on Computational learning theory
Function learning from interpolation
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Approximation and learning of convex superpositions
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Scale-sensitive dimensions, uniform convergence, and learnability
Journal of the ACM (JACM)
Learnability in Hilbert spaces with reproducing kernels
Journal of Complexity
Mathematical Modelling of Generalization
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
Geometric Methods in the Analysis of Glivenko-Cantelli Classes
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Aspects of discrete mathematics and probability in the theory of machine learning
Discrete Applied Mathematics
Vapnik-chervonenkis generalization bounds for real valued neural networks
Neural Computation
Differential privacy and the fat-shattering dimension of linear queries
APPROX/RANDOM'10 Proceedings of the 13th international conference on Approximation, and 14 the International conference on Randomization, and combinatorial optimization: algorithms and techniques
PAC learnability under non-atomic measures: A problem by Vidyasagar
Theoretical Computer Science
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