Interpolation of operators
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Boosting a weak learning algorithm by majority
Information and Computation
The hardness of approximate optima in lattices, codes, and systems of linear equations
Journal of Computer and System Sciences - Special issue: papers from the 32nd and 34th annual symposia on foundations of computer science, Oct. 2–4, 1991 and Nov. 3–5, 1993
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
On the influence of the kernel on the consistency of support vector machines
The Journal of Machine Learning Research
Support Vector Machine Soft Margin Classifiers: Error Analysis
The Journal of Machine Learning Research
Multi-kernel regularized classifiers
Journal of Complexity
Fast rates for support vector machines
COLT'05 Proceedings of the 18th annual conference on Learning Theory
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Consistency of support vector machines and other regularized kernel classifiers
IEEE Transactions on Information Theory
Kernel networks with fixed and variable widths
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I
Some comparisons of networks with radial and kernel units
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
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This paper investigates statistical performances of Support Vector Machines (SVM) and considers the problem of adaptation to the margin parameter and to complexity. In particular we provide a classifier with no tuning parameter. It is a combination of SVM classifiers. Our contribution is two-fold: (1) we propose learning rates for SVM using Sobolev spaces and build a numerically realizable aggregate that converges with same rate; (2) we present practical experiments of this method of aggregation for SVM using both Sobolev spaces and Gaussian kernels.