Eigenvalues and s-numbers
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
The covering number in learning theory
Journal of Complexity
On the Eigenspectrum of the Gram Matrix and Its Relationship to the Operator Eigenspectrum
ALT '02 Proceedings of the 13th International Conference on Algorithmic Learning Theory
Localized Rademacher Complexities
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
A few notes on statistical learning theory
Advanced lectures on machine learning
On the performance of kernel classes
The Journal of Machine Learning Research
Multi-kernel regularized classifiers
Journal of Complexity
Support Vector Machines
Fast rates for support vector machines
COLT'05 Proceedings of the 18th annual conference on Learning Theory
IEEE Transactions on Information Theory
On the eigenspectrum of the gram matrix and the generalization error of kernel-PCA
IEEE Transactions on Information Theory
A note on extending generalization bounds for binary large-margin classifiers to multiple classes
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
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In this paper, we present a new technique for bounding local Rademacher averages of function classes induced by a loss function and a reproducing kernel Hilbert space (RKHS). At the heart of this technique lies the observation that certain expectations of random entropy numbers can be bounded by the eigenvalues of the integral operator associated with the RKHS. We then work out the details of the new technique by establishing two new oracle inequalities for support vector machines, which complement and generalize previous results.