An Online Stochastic Kernel Machine for Robust Signal Classification
arXiv:1905.07686v23 citations
Originality Incremental advance
AI Analysis
This work addresses robust signal classification for applications like telecommunications or sensor networks, but appears incremental as it builds on existing online kernel methods.
The authors tackled robust signal classification by developing a novel online kernel machine that uses consensus-based optimization to guide decision functions in a reproducing kernel Hilbert space, achieving efficient modeling of stationary processes.
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.