Online learning from a signal processing perspective There is increased interest in kernel
learning algorithms in neural networks and a growing need for nonlinear adaptive …

This paper discusses an information theoretic approach of designing sparse kernel adaptive
filters. To determine useful data to be learned and remove redundant ones, a subjective …

This article presents a general tool for convexly constrained parameter/function estimation
both for classification and regression tasks, in a timeadaptive setting and in (infinite …

Over the last decade, kernel methods for nonlinear processing have successfully been used
in the machine learning community. The primary mathematical tool employed in these …

The combination of the famed kernel trick and affine projection algorithms (APAs) yields
powerful nonlinear extensions, named collectively here, KAPA. This paper is a follow-up …

The kernel least-mean-square (KLMS) algorithm is a popular algorithm in nonlinear
adaptive filtering due to its simplicity and robustness. In kernel adaptive filters, the statistics …

We present a novel diffusion scheme for online kernel-based learning over networks. So far,
a major drawback of any online learning algorithm, operating in a reproducing kernel Hilbert …

Adaptive filtering algorithms operating in reproducing kernel Hilbert spaces have
demonstrated superiority over their linear counterpart for nonlinear system identification …

In this paper, we address the problem of reconstructing coverage maps from path-loss
measurements in cellular networks. We propose and evaluate two kernel-based adaptive …

Online learning has been at the center of focus in signal processing learning tasks for many
decades, since the early days of the LMS and Kalman filtering and it has already found its …