An Online Stochastic Kernel Machine for Robust Signal Classification

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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Bibliographic Details
Main Author Raj, Raghu G
Format Journal Article
LanguageEnglish
Published 18.05.2019
Subjects
Computer Science - Learning
Statistics - Machine Learning
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Summary: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.
DOI:10.48550/arxiv.1905.07686