Asymptotic theory of finite dimensional normed spaces
Asymptotic theory of finite dimensional normed spaces
Improving the sample complexity using global data
IEEE Transactions on Information Theory
Diffusion Kernels on Statistical Manifolds
The Journal of Machine Learning Research
Learning Bounds for Kernel Regression Using Effective Data Dimensionality
Neural Computation
Using the doubling dimension to analyze the generalization of learning algorithms
Journal of Computer and System Sciences
Oracle inequalities for support vector machines that are based on random entropy numbers
Journal of Complexity
On the convergence rate of lp-norm multiple kernel learning
The Journal of Machine Learning Research
Hi-index | 0.00 |
We present sharp bounds on the localized Rademacher averages of the unit ball in a reproducing kernel Hilbert space in terms of the eigenvalues of the integral operator associated with the kernel. We use this result to estimate the performance of the empirical minimization algorithm when the base class is the unit ball of the reproducing kernel Hilbert space.