Ten lectures on wavelets
Acceleration of stochastic approximation by averaging
SIAM Journal on Control and Optimization
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Leave-one-out bounds for kernel methods
Neural Computation
On the influence of the kernel on the consistency of support vector machines
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Model Selection for Regularized Least-Squares Algorithm in Learning Theory
Foundations of Computational Mathematics
Learning from Examples as an Inverse Problem
The Journal of Machine Learning Research
Foundations of Computational Mathematics
Capacity of reproducing kernel spaces in learning theory
IEEE Transactions on Information Theory
On the generalization ability of on-line learning algorithms
IEEE Transactions on Information Theory
Derivative reproducing properties for kernel methods in learning theory
Journal of Computational and Applied Mathematics
Classification with Gaussians and Convex Loss
The Journal of Machine Learning Research
Logistic classification with varying Gaussians
Computers & Mathematics with Applications
Online crowdsourcing subjective image quality assessment
Proceedings of the 20th ACM international conference on Multimedia
Conditional quantiles with varying Gaussians
Advances in Computational Mathematics
Learning theory approach to minimum error entropy criterion
The Journal of Machine Learning Research
| Hi-index | 754.84 |
In this paper, some new probabilistic upper bounds are presented for the online learning algorithm proposed in [1], and more generally for linear stochastic approximations in Hilbert spaces.With these upper bounds not only does one recover almost sure convergence, but also relaxes the square summable condition on the step size appeared in the early work. Furthermore two probabilistic upper bounds are given for an averaging process, both of which achieve the same rate with respect to sample size as in "batch learning" algorithms, and one of which is tight in both sample size and regularization parameter.