More theorems about scale-sensitive dimensions and learning
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Scale-sensitive dimensions, uniform convergence, and learnability
Journal of the ACM (JACM)
Learning in Neural Networks: Theoretical Foundations
Learning in Neural Networks: Theoretical Foundations
Statistical Sufficiency for Classes in Empirical L2 Spaces
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Covering numbers for real-valued function classes
IEEE Transactions on Information Theory
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We use geometric methods to investigate several fundamental problems in machine learning. We present a new bound on the Lp coveringn umbers of Glivenko-Cantelli classes for 1 ≤ p