Communications of the ACM
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)
Computational learning theory: an introduction
Computational learning theory: an introduction
Decision theoretic generalizations of the PAC model for neural net and other learning applications
Information and Computation
An introduction to computational learning theory
An introduction to computational learning theory
More theorems about scale-sensitive dimensions and learning
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Polynomial bounds for VC dimension of sigmoidal and general Pfaffian neural networks
Journal of Computer and System Sciences - Special issue: dedicated to the memory of Paris Kanellakis
Scale-sensitive dimensions, uniform convergence, and learnability
Journal of the ACM (JACM)
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A sharp concentration inequality with application
Random Structures & Algorithms
A Theory of Learning and Generalization: With Applications to Neural Networks and Control Systems
A Theory of Learning and Generalization: With Applications to Neural Networks and Control Systems
Learning in Neural Networks: Theoretical Foundations
Learning in Neural Networks: Theoretical Foundations
Rademacher and Gaussian Complexities: Risk Bounds and Structural Results
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Some Local Measures of Complexity of Convex Hulls and Generalization Bounds
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
Localized Rademacher Complexities
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
IEEE Transactions on Information Theory
Structural risk minimization over data-dependent hierarchies
IEEE Transactions on Information Theory
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This paper surveys certain developments in the use of probabilistic techniques for the modelling of generalization. Some of the main methods and key results are discussed. Many details are omitted, the aim being to give a high-level overview of the types of approaches taken and methods used.