# 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.

## Key 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.

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Source: https://tomesphere.com/paper/1905.07686