Asymptotic theory of finite dimensional normed spaces
Asymptotic theory of finite dimensional normed spaces
Sphere packing numbers for subsets of the Boolean n-cube with bounded Vapnik-Chervonenkis dimension
Journal of Combinatorial Theory Series A
Scale-sensitive dimensions, uniform convergence, and learnability
Journal of the ACM (JACM)
Prediction, learning, uniform convergence, and scale-sensitive dimensions
Journal of Computer and System Sciences - Special issue on the eighth annual workshop on computational learning theory, July 5–8, 1995
Learning in Neural Networks: Theoretical Foundations
Learning in Neural Networks: Theoretical Foundations
Rademacher averages and phase transitions in Glivenko-Cantelli classes
IEEE Transactions on Information Theory
Improving the sample complexity using global data
IEEE Transactions on Information Theory
A few notes on statistical learning theory
Advanced lectures on machine learning
Inapproximability for VCG-based combinatorial auctions
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
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In this article we introduce a new combinatorial parameter which generalizes the VC dimension and the fat-shattering dimension, and extends beyond the function-class setup. Using this parameter we establish entropy bounds for subsets of the n-dimensional unit cube, and in particular, we present new bounds on the empirical covering numbers and gaussian averages associated with classes of functions in terms of the fat-shattering dimension.