Model Selection and Error Estimation
Machine Learning
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
Agnostic Learning Nonconvex Function Classes
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
Efficient agnostic learning of neural networks with bounded fan-in
IEEE Transactions on Information Theory - Part 2
Improving the sample complexity using global data
IEEE Transactions on Information Theory
Mathematical Modelling of Generalization
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
Some Local Measures of Complexity of Convex Hulls and Generalization Bounds
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
Agnostic Learning Nonconvex Function Classes
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
An introduction to boosting and leveraging
Advanced lectures on machine learning
Generalization error bounds for Bayesian mixture algorithms
The Journal of Machine Learning Research
On the rate of convergence of regularized boosting classifiers
The Journal of Machine Learning Research
Generalization Error Bounds for Threshold Decision Lists
The Journal of Machine Learning Research
Computable Shell Decomposition Bounds
The Journal of Machine Learning Research
Selective Rademacher Penalization and Reduced Error Pruning of Decision Trees
The Journal of Machine Learning Research
Learning Bounds for Kernel Regression Using Effective Data Dimensionality
Neural Computation
Nonparametric Quantile Estimation
The Journal of Machine Learning Research
Aspects of discrete mathematics and probability in the theory of machine learning
Discrete Applied Mathematics
An RKHS for multi-view learning and manifold co-regularization
Proceedings of the 25th international conference on Machine learning
Oracle inequalities for support vector machines that are based on random entropy numbers
Journal of Complexity
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We investigate the behaviour of global and local Rademacher averages. We present new error bounds which are based on the local averages and indicate how data-dependent local averages can be estimated without a priori knowledge of the class at hand.