An Online Stochastic Kernel Machine for Robust Signal Classification

TL;DR

This paper introduces an online stochastic kernel machine that uses consensus-based optimization to improve robust signal classification by evolving decision functions within a reproducing kernel Hilbert space.

Contribution

The paper proposes a novel online kernel machine framework utilizing consensus optimization for robust signal classification.

Findings

Efficient modeling of stationary processes.

Improved robustness in signal classification.

Novel online kernel learning algorithm.

Abstract

We present a novel variation of online kernel machines in which we exploit a consensus based optimization mechanism to guide the evolution of decision functions drawn from a reproducing kernel Hilbert space, which efficiently models the observed stationary process.