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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Main Author | |
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Format | Journal Article |
Language | English |
Published |
18.05.2019
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Subjects | |
Online Access | Get full text |
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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. |
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DOI: | 10.48550/arxiv.1905.07686 |